DPImageBench: A Unified Benchmark for Differentially Private Image Synthesis
Chen Gong, Kecen Li, Zinan Lin, Tianhao Wang
CCS 2025 | July 2025
Privacy Preserving Machine Learning Innovation
出版物
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The Canary’s Echo: Auditing Privacy Risks of LLM-Generated Synthetic Text
Matthieu Meeus, Lukas Wutschitz, Santiago Zanella-Béguelin, Reza Shokri, Shruti Tople
February 2025
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Differentially Private Synthetic Data via APIs 3: Using Simulators Instead of Foundation Models
Zinan Lin, Tadas Baltrusaitis, Sergey Yekhanin
February 2025
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Inferentially-Private Private Information
Shuaiqi Wang, Shuran Zheng, Zinan Lin, Giulia Fanti, Zhiwei Steven Wu
WWW 2025 | February 2025
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MMDT: Decoding the Trustworthiness and Safety of Multimodal Foundation Models
Chejian Xu, Jiawei Zhang, Zhaorun Chen, Chulin Xie, Mintong Kang, Zhuowen Yuan, Zidi Xiong, Chenhui Zhang, Lingzhi Yuan, Yi Zeng, Peiyang Xu, Chengquan Guo, Andy Zhou, Jeffrey Ziwei Tan, Zhun Wang, Alexander Xiong, Xuandong Zhao, Yu Gai, Francesco Pinto, Yujin Potter, Zhen Xiang, Zinan Lin, Dan Hendrycks, Dawn Song, Bo Li
ICLR 2025 | February 2025
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Closed-Form Bounds for DP-SGD against Record-level Inference
Giovanni Cherubin, Boris Köpf, Andrew Paverd, Shruti Tople, Lukas Wutschitz, Santiago Zanella-Béguelin
USENIX Security Symposium | August 2024
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Differentially Private Synthetic Data via Foundation Model APIs 2: Text
Chulin Xie, Zinan Lin, Arturs Backurs, Sivakanth Gopi, Da Yu, Huseyin Inan, Harsha Nori, Haotian Jiang, Huishuai Zhang, Yin Tat Lee, Bo Li, Sergey Yekhanin
ICML 2024 | July 2024
Spotlight
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Statistic Maximal Leakage
Shuaiqi Wang, Zinan Lin, Giulia Fanti
IEEE International Symposium on Information Theory 2024 | July 2024
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Dataset and Lessons Learned from the 2024 SaTML LLM Capture-the-Flag Competition
Edoardo Debenedetti, Javier Rando, Daniel Paleka, Fineas Silaghi, Dragos Albastroiu, Niv Cohen, Yuval Lemberg, Reshmi Ghosh, Ahmed Salem, Rui Wen, Giovanni Cherubin, Santiago Zanella-Béguelin, Robin Schmid, Victor Klemm, Takahiro Miki, Chenhao Li, Stefan Kraft, Mario Fritz, Florian Tramer, Sahar Abdelnabi, Lea Schönherr
NeurIPS 2024 | June 2024
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Differentially Private Synthetic Data via Foundation Model APIs 1: Images
Zinan Lin, Sivakanth Gopi, Janardhan (Jana) Kulkarni, Harsha Nori, Sergey Yekhanin
ICLR 2024 | May 2024
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Privacy-Preserving In-Context Learning with Differentially Private Few-Shot Generation
Xinyu Tang, Richard Shin, Huseyin Inan, Andre Manoel, Fatemehsadat Mireshghallah, Zinan Lin, Sivakanth Gopi, Janardhan (Jana) Kulkarni, Robert Sim
ICLR 2024 | May 2024
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Invariant Aggregator for Defending against Federated Backdoor Attacks
Xiaoya Wang, Dimitrios Dimitriadis, Oluwasanmi Koyejo, Shruti Tople
International Conference on Artificial Intelligence and Statistics, AISTATS | May 2024
編集者: Sanjoy Dasgupta, Stephan Mandt, Yingzhen Li
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Selective Pre-training for Private Fine-tuning
Da Yu, Sivakanth Gopi, Janardhan (Jana) Kulkarni, Zinan Lin, Saurabh Naik, Tomasz Lukasz Religa, Jian Yin, Huishuai Zhang
TMLR | May 2024
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Summary Statistic Privacy in Data Sharing
Zinan Lin, Shuaiqi Wang, Vyas Sekar, Giulia Fanti
IEEE Journal on Selected Areas in Information Theory | March 2024
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Advancing differential privacy: where we are now and future directions for real-world deployment
Rachel Cummings, Damien Desfontaines, David Evans, Roxana Geambasu, Yangsibo Huang, Matthew Jagielski, Peter Kairouz, Gautam Kamath, Sewoong Oh, Olga Ohrimenko, Nicolas Papernot, Ryan Rogers, Milan Shen, Shuang Song, Weijie Su, Andreas Terzis, Abhradeep Thakurta, Sergei Vassilvitskii, Yu-Xiang Wang, Li Xiong, Da Yu, Sergey Yekhanin, Huanyu Zhang, Wanrong Zhang
Harvard Data Science Review | January 2024, 巻6(1)
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Training Private and Efficient Language Models with Synthetic Data from LLMs
Da Yu, Arturs Backurs, Sivakanth Gopi, Huseyin Inan, Janardhan (Jana) Kulkarni, Zinan Lin, Chulin Xie, Huishuai Zhang, Wanrong Zhang
NeurIPS 2023 SoLaR Workshop | December 2023
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Secure Floating-Point Training
Deevashwer Rathee, Anwesh Bhattacharya, Divya Gupta, Rahul Sharma, Dawn Song
Usenix Security Symposium 2023 | August 2023
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Project Florida: Federated Learning Made Easy
Daniel Madrigal, Andre Manoel, Jialei Chen, Nalin Singal, Robert Sim
| July 2023
Microsoft による投稿
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Confidential Computing within an AI Accelerator
Kapil Vaswani, Stavros Volos, Cédric Fournet, Antonio Nino Diaz, Ken Gordon, Balaji Vembu, Sam Webster, David Chisnall, Saurabh Kulkarni, Graham Cunningham , Richard Osborne , Daniel Wilkinson
2023 USENIX Annual Technical Conference | July 2023
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Bayesian Estimation of Differential Privacy
Santiago Zanella-Béguelin, Lukas Wutschitz, Shruti Tople, Ahmed Salem, Victor Ruehle, Andrew Paverd, Mohammad Naseri, Boris Köpf, Daniel Jones
2023 International Conference on Machine Learning | July 2023
編集者: Barbara Engelhardt, Emma Brunskill, Kyunghyun Cho
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DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Models
Boxin Wang, Weixin Chen, Hengzhi Pei, Chulin Xie, Mintong Kang, Chenhui Zhang, Chejian Xu, Zidi Xiong, Ritik Dutta, Rylan Schaeffer, Sang T. Truong, Simran Arora, Mantas Mazeika, Dan Hendrycks, Zinan Lin, Yu Cheng, Sanmi Koyejo, Dawn Song, Bo Li
NeurIPS 2023 | June 2023
Outstanding Paper Award
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Analyzing Leakage of Personally Identifiable Information in Language Models
Nils Lukas, Ahmed Salem, Robert Sim, Shruti Tople, Lukas Wutschitz, Santiago Zanella-Béguelin
2023 IEEE Symposium on Security and Privacy | May 2023
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Exploring the Limits of Differentially Private Deep Learning with Group-wise Clipping
Jiyan He, Xuechen Li, Da Yu, Huishuai Zhang, Janardhan (Jana) Kulkarni, Yin Tat Lee, Arturs Backurs, Nenghai Yu, Jiang Bian
International Conference on Learning Representations (ICLR) 2023 | May 2023
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SoK: Let the Privacy Games Begin! A Unified Treatment of Data Inference Privacy in Machine Learning
Ahmed Salem, Giovanni Cherubin, David Evans, Boris Köpf, Andrew Paverd, Anshuman Suri, Shruti Tople, Santiago Zanella-Béguelin
2023 IEEE Symposium on Security and Privacy | May 2023
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Algorithmic Aspects of the Log-Laplace Transform and a Non-Euclidean Proximal Sampler
Sivakanth Gopi, Yin Tat Lee, Daogao Liu, Rouqi Shen, Kevin Tian
COLT 2023 | February 2023
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Differentially Private Model Compression
Fatemehsadat Mireshghallah, Arturs Backurs, Huseyin Inan, Lukas Wutschitz, Janardhan (Jana) Kulkarni
NeurIPS 2022 | November 2022
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When does differentially private learning not suffer in high dimensions?
Xuechen Li, Daogao Liu, Tatsunori Hashimoto, Huseyin Inan, Janardhan (Jana) Kulkarni, Yin Tat Lee, Abhradeep Guha Thakurta
NeurIPS’22 | November 2022
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Membership Inference Attacks and Generalization: A Causal Perspective
Teodora Baluta, Shiqi Shen, S. Hitarth, Shruti Tople, Prateek Saxena
Computer and Communications Security (CCS) | November 2022
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Heterogeneous Ensemble Knowledge Transfer for Training Large Models in Federated Learning
Yae Jee Cho, Andre Manoel, Gauri Joshi, Robert Sim, Dimitrios Dimitriadis
IJCAI-ECAI’22 | August 2022
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SIMC: ML Inference Secure Against Malicious Clients at Semi-Honest Cost
Nishanth Chandran, Divya Gupta, Sai Lakshmi Bhavana Obbattu, Akash Shah
USENIX Security | August 2022
USENIX Security 2022
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dp-transformers: Training transformer models with differential privacy
Huseyin Inan, Andre Manoel, Lukas Wutschitz
July 2022
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UserIdentifier: Implicit User Representations for Simple and Effective Personalized Sentiment Analysis
Fatemehsadat Mireshghallah, Vaishnavi Shrivastava, Milad Shokouhi, Taylor Berg-Kirkpatrick, Robert Sim, Dimitrios Dimitriadis
2022 Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL’22) | July 2022
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Private Convex Optimization in General Norms
Sivakanth Gopi, Yin Tat Lee, Daogao Liu, Rouqi Shen, Kevin Tian
SODA 2023 | July 2022
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Circuit-PSI with Linear Complexity via Relaxed Batch OPPRF
Nishanth Chandran, Divya Gupta, Akash Shah
22nd Privacy Enhancing Technologies Symposium (PETS 2022) | June 2022
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Personalized Federated Learning with Adaptive Batchnorm for Healthcare
Wang Lu, Jindong Wang, Yiqiang Chen, Xin Qin, Renjun Xu, Dimitrios Dimitriadis, Tao Qin
IEEE Transactions on Big Data | May 2022
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SECFLOAT: Accurate Floating-Point meets Secure 2-Party Computation
Deevashwer Rathee, Anwesh Bhattacharya, Rahul Sharma, Divya Gupta, Nishanth Chandran, Aseem Rastogi
43rd IEEE Symposium on Security and Privacy (IEEE S&P 2022) | May 2022
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Differentially private fine-tuning of language models
Da Yu, Saurabh Naik, Arturs Backurs, Sivakanth Gopi, Huseyin Inan, Gautam Kamath, Janardhan (Jana) Kulkarni, Yin Tat Lee, Andre Manoel, Lukas Wutschitz, Sergey Yekhanin, Huishuai Zhang
ICLR 2022 | April 2022
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FLUTE: A Scalable, Extensible Framework for High-Performance Federated Learning Simulations
Dimitrios Dimitriadis, Mirian Hipolito Garcia, Daniel Madrigal, Andre Manoel, Robert Sim
March 2022
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Private Convex Optimization via Exponential Mechanism
Sivakanth Gopi, Yin Tat Lee, Daogao Liu
COLT 2022 | March 2022
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Differentially private n-gram extraction
Kunho Kim, Sivakanth Gopi, Janardhan (Jana) Kulkarni, Sergey Yekhanin
NeurIPS 2021 | December 2021
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Tackling Dynamics in Federated Incremental Learning with Variational Embedding Rehearsal
Tae Jin Park, Kenichi Kumatani, Dimitrios Dimitriadis
October 2021
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Secure Featurization and Applications to Secure Phishing Detection
Akash Shah, Nishanth Chandran, Divya Gupta, Mesfin Dema, Arun Gururajan, Huan Yu
CCSW 2021: The ACM Cloud Computing Security Workshop | October 2021
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Membership Inference Attacks Against NLP Classification Models
Virat Shejwalkar, Huseyin Inan, Amir Houmansadr, Robert Sim
NeurIPS 2021 Workshop Privacy in Machine Learning | September 2021
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Leakage of Dataset Properties in Multi-Party Machine Learning
Wanrong Zhang, Shruti Tople, Olga Ohrimenko
Usenix Security | August 2021
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Accuracy, Interpretability, and Differential Privacy via Explainable Boosting
Harsha Nori, Rich Caruana, Zhiqi Bu, Judy Hanwen Shen, Janardhan (Jana) Kulkarni
2021 International Conference on Machine Learning | July 2021
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Domain Generalization using Causal Matching
Divyat Mahajan, Shruti Tople, Amit Sharma
International Conference on Machine Learning (ICML) | July 2021
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Grey-box Extraction of Natural Language Models
Santiago Zanella-Béguelin, Shruti Tople, Andrew Paverd, Boris Köpf
International Conference on Machine Learning | July 2021
編集者: Marina Meila and Tong Zhang
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Membership Inference on Word Embedding and Beyond
Saeed Mahloujifar, Huseyin A. Inan, Melissa Chase, Esha Ghosh, Marcello Hasegawa
June 2021
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Numerical Composition of Differential Privacy
Sivakanth Gopi, Yin Tat Lee, Lukas Wutschitz
NeurIPS 2021 | June 2021
Spotlight at NeurIPS 2021.
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Privacy Regularization: Joint Privacy-Utility Optimization in Language Models
Fatemehsadat Mireshghallah, Huseyin Inan, Marcello Hasegawa, Victor Rühle, Taylor Berg-Kirkpatrick, Robert Sim
2021 Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL 2021) | June 2021
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Causally Constrained Data Synthesis for Private Data Release
Varun Chandrasekaran, Darren Edge, Somesh Jha, Amit Sharma, Cheng Zhang, Shruti Tople
Distributed and Private Machine Learning (DPML) Workshop at ICLR’21 | May 2021
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On Privacy and Confidentiality of Communications in Organizational Graphs
Masoumeh Shafieinejad, Huseyin A. Inan (huinan), Marcello Hasegawa, Robert Sim
May 2021
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Efficient Linear Multiparty PSI and Extensions to Circuit/Quorum PSI
Nishanth Chandran, Nishka Dasgupta, Divya Gupta, Sai Lakshmi Bhavana Obbattu, Sruthi Sekar, Akash Shah
28th Annual Conference on Computer and Communications Security (ACM CCS 2021) | May 2021
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Private Non-smooth Empirical Risk Minimization and Stochastic Convex Optimization in Subquadratic Steps
Janardhan (Jana) Kulkarni, Yin Tat Lee, Daogao Liu
NeurIPS 2021 | March 2021
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Training Data Leakage Analysis in Language Models
Huseyin A. Inan, Osman Ramadan, Lukas Wutschitz, Daniel Jones, Victor Rühle, James Withers, Robert Sim
February 2021
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Differentially Private Correlation Clustering
Mark Bun, Marek Eliás, Janardhan (Jana) Kulkarni
2021 International Conference on Machine Learning | February 2021
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Fast and Memory Efficient Differentially Private-SGD via JL Projections
Zhiqi Bu, Sivakanth Gopi, Janardhan (Jana) Kulkarni, Yin Tat Lee, Judy Hanwen Shen, Uthaipon Tantipongpipat
NeurIPS 2021 | February 2021
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SIRNN: A Math Library for Secure RNN Inference
Deevashwer Rathee, Mayank Rathee, Rahul Kranti Kiran Goli, Divya Gupta, Rahul Sharma, Nishanth Chandran, Aseem Rastogi
42nd IEEE Symposium on Security and Privacy (IEEE S&P 2021) | February 2021
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Property Inference from Poisoning
Melissa Chase, Esha Ghosh, Saeed Mahloujifar
January 2021
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Function Secret Sharing for Mixed-Mode and Fixed-Point Secure Computation
Elette Boyle, Nishanth Chandran, Niv Gilboa, Divya Gupta, Yuval Ishai, Nishant Kumar, Mayank Rathee
EUROCRYPT 2021 | January 2021
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Secure Medical Image Analysis with CrypTFlow
Javier Alvarez-Valle, Pratik Bhatu, Nishanth Chandran, Divya Gupta, Aditya Nori, Aseem Rastogi, Mayank Rathee, Rahul Sharma, Shubham Ugare
NeurIPS PPML Workshop | December 2020
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The Connection between Out-of-Distribution Generalization and Privacy of ML Models
Divyat Mahajan, Shruti Tople, Amit Sharma
Privacy Preserving Machine Learning (PriML/PPML) Workshop at NeurIPS’20 | December 2020
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Analyzing Information Leakage of Updates to Natural Language Models
Santiago Zanella-Béguelin, Lukas Wutschitz, Shruti Tople, Victor Ruehle, Andrew Paverd, Olga Ohrimenko, Boris Köpf, Marc Brockschmidt
ACM Conference on Computer and Communication Security (CCS) | November 2020
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An Empirical Study on the Intrinsic Privacy of SGD
Stephanie Hyland, Shruti Tople
Theory and Practice of Differential Privacy (CCS Worshop) | November 2020
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CrypTFlow2: Practical 2-Party Secure Inference
Deevashwer Rathee, Mayank Rathee, Nishant Kumar, Nishanth Chandran, Divya Gupta, Aseem Rastogi, Rahul Sharma
27th Annual Conference on Computer and Communications Security (ACM CCS 2020) | August 2020
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Visor: Privacy-Preserving Video Analytics as a Cloud Service
Rishabh Poddar, Ganesh Ananthanarayanan, Srinath Setty, Stavros Volos, Raluca Ada Popa
29th USENIX Security Symposium | August 2020
CSAW 2020 Applied Research Competition Award (Runner-up)
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Alleviating Privacy Attacks via Causal Learning
Shruti Tople, Amit Sharma, Aditya V. Nori
International Conference on Machine Learning (ICML) | July 2020
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Differentially Private Set Union
Sivakanth Gopi, Pankaj Gulhane, Janardhan (Jana) Kulkarni, Judy Hanwen Shen, Milad Shokouhi, Sergey Yekhanin
International Conference on Machine Learning 2020 | July 2020
Contributed talk at TPDP 2020. Also appeared in the Journal of Privacy and Confidentiality (JPC).
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Locally Private Hypothesis Selection
Sivakanth Gopi, Gautam Kamath, Janardhan (Jana) Kulkarni, Aleksandar Nikolov, Zhiwei Steven Wu, Huanyu Zhang
Conference on Learning Theory (COLT) 2020 | July 2020
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Privately Learning Markov Random Fields
Huanyu Zhang, Gautam Kamath, Janardhan (Jana) Kulkarni, Zhiwei Steven Wu
ICML 2020 | July 2020
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CrypTFlow: Secure TensorFlow Inference
Nishant Kumar, Mayank Rathee, Nishanth Chandran, Divya Gupta, Aseem Rastogi, Rahul Sharma
IEEE Symposium on Security and Privacy | May 2020
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Differentially private release of synthetic graphs
Marek Eliáš, Michael Kapralov, Janardhan (Jana) Kulkarni, Yin Tat Lee
2020 Symposium on Discrete Algorithms | January 2020
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Locally Private Gaussian Estimation
Matthew Joseph, Janardhan (Jana) Kulkarni, Jieming Mao, Zhiwei Steven Wu
November 2019
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An Algorithmic Framework For Differentially Private Data Analysis on Trusted Processors
Joshua Allen, Bolin Ding, Janardhan (Jana) Kulkarni, Harsha Nori, Olga Ohrimenko, Sergey Yekhanin
NeurIPS 2019 | August 2019
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EzPC: Programmable, Efficient, and Scalable Secure Two-Party Computation for Machine Learning
Nishanth Chandran, Divya Gupta, Aseem Rastogi, Rahul Sharma, Shardul Tripathi
IEEE European Symposium on Security and Privacy | February 2019
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SecureNN: Efficient and Private Neural Network Training
Sameer Wagh, Divya Gupta, Nishanth Chandran
Privacy Enhancing Technologies Symposium | February 2019
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Graviton: Trusted Execution Environments on GPUs
Stavros Volos, Kapil Vaswani, Rordigo Bruno
13th USENIX Symposium on Operating Systems Design and Implementation | October 2018
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Collecting Telemetry Data Privately
Bolin Ding, Janardhan (Jana) Kulkarni, Sergey Yekhanin
Advances in Neural Information Processing Systems 30 | December 2017
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Oblivious Multi-Party Machine Learning on Trusted Processors
Olya Ohrimenko, Felix Schuster, Cédric Fournet, Aastha Mehta, Sebastian Nowozin, Kapil Vaswani, Manuel Costa
The 25th USENIX Security Symposium 2016 | July 2016
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VC3: Trustworthy Data Analytics in the Cloud using SGX
Felix Schuster, Manuel Costa, Cédric Fournet, Christos Gkantsidis, Marcus Peinado, Gloria Mainar-Ruiz, Mark Russinovich
36th IEEE Symposium on Security and Privacy | May 2015
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DPImageBench: A Unified Benchmark for Differentially Private Image Synthesis
Chen Gong, Kecen Li, Zinan Lin, Tianhao Wang
CCS 2025 | July 2025
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The Canary’s Echo: Auditing Privacy Risks of LLM-Generated Synthetic Text
Matthieu Meeus, Lukas Wutschitz, Santiago Zanella-Béguelin, Reza Shokri, Shruti Tople
February 2025
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Differentially Private Synthetic Data via APIs 3: Using Simulators Instead of Foundation Models
Zinan Lin, Tadas Baltrusaitis, Sergey Yekhanin
February 2025
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Inferentially-Private Private Information
Shuaiqi Wang, Shuran Zheng, Zinan Lin, Giulia Fanti, Zhiwei Steven Wu
WWW 2025 | February 2025
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MMDT: Decoding the Trustworthiness and Safety of Multimodal Foundation Models
Chejian Xu, Jiawei Zhang, Zhaorun Chen, Chulin Xie, Mintong Kang, Zhuowen Yuan, Zidi Xiong, Chenhui Zhang, Lingzhi Yuan, Yi Zeng, Peiyang Xu, Chengquan Guo, Andy Zhou, Jeffrey Ziwei Tan, Zhun Wang, Alexander Xiong, Xuandong Zhao, Yu Gai, Francesco Pinto, Yujin Potter, Zhen Xiang, Zinan Lin, Dan Hendrycks, Dawn Song, Bo Li
ICLR 2025 | February 2025
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Closed-Form Bounds for DP-SGD against Record-level Inference
Giovanni Cherubin, Boris Köpf, Andrew Paverd, Shruti Tople, Lukas Wutschitz, Santiago Zanella-Béguelin
USENIX Security Symposium | August 2024
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Differentially Private Synthetic Data via Foundation Model APIs 2: Text
Chulin Xie, Zinan Lin, Arturs Backurs, Sivakanth Gopi, Da Yu, Huseyin Inan, Harsha Nori, Haotian Jiang, Huishuai Zhang, Yin Tat Lee, Bo Li, Sergey Yekhanin
ICML 2024 | July 2024
Spotlight
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Statistic Maximal Leakage
Shuaiqi Wang, Zinan Lin, Giulia Fanti
IEEE International Symposium on Information Theory 2024 | July 2024
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Dataset and Lessons Learned from the 2024 SaTML LLM Capture-the-Flag Competition
Edoardo Debenedetti, Javier Rando, Daniel Paleka, Fineas Silaghi, Dragos Albastroiu, Niv Cohen, Yuval Lemberg, Reshmi Ghosh, Ahmed Salem, Rui Wen, Giovanni Cherubin, Santiago Zanella-Béguelin, Robin Schmid, Victor Klemm, Takahiro Miki, Chenhao Li, Stefan Kraft, Mario Fritz, Florian Tramer, Sahar Abdelnabi, Lea Schönherr
NeurIPS 2024 | June 2024
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Privacy-Preserving In-Context Learning with Differentially Private Few-Shot Generation
Xinyu Tang, Richard Shin, Huseyin Inan, Andre Manoel, Fatemehsadat Mireshghallah, Zinan Lin, Sivakanth Gopi, Janardhan (Jana) Kulkarni, Robert Sim
ICLR 2024 | May 2024
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Differentially Private Synthetic Data via Foundation Model APIs 1: Images
Zinan Lin, Sivakanth Gopi, Janardhan (Jana) Kulkarni, Harsha Nori, Sergey Yekhanin
ICLR 2024 | May 2024
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Invariant Aggregator for Defending against Federated Backdoor Attacks
Xiaoya Wang, Dimitrios Dimitriadis, Oluwasanmi Koyejo, Shruti Tople
International Conference on Artificial Intelligence and Statistics, AISTATS | May 2024
編集者: Sanjoy Dasgupta, Stephan Mandt, Yingzhen Li
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Summary Statistic Privacy in Data Sharing
Zinan Lin, Shuaiqi Wang, Vyas Sekar, Giulia Fanti
IEEE Journal on Selected Areas in Information Theory | March 2024
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Training Private and Efficient Language Models with Synthetic Data from LLMs
Da Yu, Arturs Backurs, Sivakanth Gopi, Huseyin Inan, Janardhan (Jana) Kulkarni, Zinan Lin, Chulin Xie, Huishuai Zhang, Wanrong Zhang
NeurIPS 2023 SoLaR Workshop | December 2023
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Project Florida: Federated Learning Made Easy
Daniel Madrigal, Andre Manoel, Jialei Chen, Nalin Singal, Robert Sim
| July 2023
Microsoft による投稿
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Bayesian Estimation of Differential Privacy
Santiago Zanella-Béguelin, Lukas Wutschitz, Shruti Tople, Ahmed Salem, Victor Ruehle, Andrew Paverd, Mohammad Naseri, Boris Köpf, Daniel Jones
2023 International Conference on Machine Learning | July 2023
編集者: Barbara Engelhardt, Emma Brunskill, Kyunghyun Cho
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DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Models
Boxin Wang, Weixin Chen, Hengzhi Pei, Chulin Xie, Mintong Kang, Chenhui Zhang, Chejian Xu, Zidi Xiong, Ritik Dutta, Rylan Schaeffer, Sang T. Truong, Simran Arora, Mantas Mazeika, Dan Hendrycks, Zinan Lin, Yu Cheng, Sanmi Koyejo, Dawn Song, Bo Li
NeurIPS 2023 | June 2023
Outstanding Paper Award
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Analyzing Leakage of Personally Identifiable Information in Language Models
Nils Lukas, Ahmed Salem, Robert Sim, Shruti Tople, Lukas Wutschitz, Santiago Zanella-Béguelin
2023 IEEE Symposium on Security and Privacy | May 2023
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SoK: Let the Privacy Games Begin! A Unified Treatment of Data Inference Privacy in Machine Learning
Ahmed Salem, Giovanni Cherubin, David Evans, Boris Köpf, Andrew Paverd, Anshuman Suri, Shruti Tople, Santiago Zanella-Béguelin
2023 IEEE Symposium on Security and Privacy | May 2023
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Exploring the Limits of Differentially Private Deep Learning with Group-wise Clipping
Jiyan He, Xuechen Li, Da Yu, Huishuai Zhang, Janardhan (Jana) Kulkarni, Yin Tat Lee, Arturs Backurs, Nenghai Yu, Jiang Bian
International Conference on Learning Representations (ICLR) 2023 | May 2023
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Differentially Private Model Compression
Fatemehsadat Mireshghallah, Arturs Backurs, Huseyin Inan, Lukas Wutschitz, Janardhan (Jana) Kulkarni
NeurIPS 2022 | November 2022
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Membership Inference Attacks and Generalization: A Causal Perspective
Teodora Baluta, Shiqi Shen, S. Hitarth, Shruti Tople, Prateek Saxena
Computer and Communications Security (CCS) | November 2022
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Heterogeneous Ensemble Knowledge Transfer for Training Large Models in Federated Learning
Yae Jee Cho, Andre Manoel, Gauri Joshi, Robert Sim, Dimitrios Dimitriadis
IJCAI-ECAI’22 | August 2022
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dp-transformers: Training transformer models with differential privacy
Huseyin Inan, Andre Manoel, Lukas Wutschitz
July 2022
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UserIdentifier: Implicit User Representations for Simple and Effective Personalized Sentiment Analysis
Fatemehsadat Mireshghallah, Vaishnavi Shrivastava, Milad Shokouhi, Taylor Berg-Kirkpatrick, Robert Sim, Dimitrios Dimitriadis
2022 Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL’22) | July 2022
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Personalized Federated Learning with Adaptive Batchnorm for Healthcare
Wang Lu, Jindong Wang, Yiqiang Chen, Xin Qin, Renjun Xu, Dimitrios Dimitriadis, Tao Qin
IEEE Transactions on Big Data | May 2022
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FLUTE: A Scalable, Extensible Framework for High-Performance Federated Learning Simulations
Dimitrios Dimitriadis, Mirian Hipolito Garcia, Daniel Madrigal, Andre Manoel, Robert Sim
March 2022
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Tackling Dynamics in Federated Incremental Learning with Variational Embedding Rehearsal
Tae Jin Park, Kenichi Kumatani, Dimitrios Dimitriadis
October 2021
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Membership Inference Attacks Against NLP Classification Models
Virat Shejwalkar, Huseyin Inan, Amir Houmansadr, Robert Sim
NeurIPS 2021 Workshop Privacy in Machine Learning | September 2021
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Leakage of Dataset Properties in Multi-Party Machine Learning
Wanrong Zhang, Shruti Tople, Olga Ohrimenko
Usenix Security | August 2021
-
Accuracy, Interpretability, and Differential Privacy via Explainable Boosting
Harsha Nori, Rich Caruana, Zhiqi Bu, Judy Hanwen Shen, Janardhan (Jana) Kulkarni
2021 International Conference on Machine Learning | July 2021
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Domain Generalization using Causal Matching
Divyat Mahajan, Shruti Tople, Amit Sharma
International Conference on Machine Learning (ICML) | July 2021
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Grey-box Extraction of Natural Language Models
Santiago Zanella-Béguelin, Shruti Tople, Andrew Paverd, Boris Köpf
International Conference on Machine Learning | July 2021
編集者: Marina Meila and Tong Zhang
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Membership Inference on Word Embedding and Beyond
Saeed Mahloujifar, Huseyin A. Inan, Melissa Chase, Esha Ghosh, Marcello Hasegawa
June 2021
-
Privacy Regularization: Joint Privacy-Utility Optimization in Language Models
Fatemehsadat Mireshghallah, Huseyin Inan, Marcello Hasegawa, Victor Rühle, Taylor Berg-Kirkpatrick, Robert Sim
2021 Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL 2021) | June 2021
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On Privacy and Confidentiality of Communications in Organizational Graphs
Masoumeh Shafieinejad, Huseyin A. Inan (huinan), Marcello Hasegawa, Robert Sim
May 2021
-
Causally Constrained Data Synthesis for Private Data Release
Varun Chandrasekaran, Darren Edge, Somesh Jha, Amit Sharma, Cheng Zhang, Shruti Tople
Distributed and Private Machine Learning (DPML) Workshop at ICLR’21 | May 2021
-
Private Non-smooth Empirical Risk Minimization and Stochastic Convex Optimization in Subquadratic Steps
Janardhan (Jana) Kulkarni, Yin Tat Lee, Daogao Liu
NeurIPS 2021 | March 2021
-
Training Data Leakage Analysis in Language Models
Huseyin A. Inan, Osman Ramadan, Lukas Wutschitz, Daniel Jones, Victor Rühle, James Withers, Robert Sim
February 2021
-
Property Inference from Poisoning
Melissa Chase, Esha Ghosh, Saeed Mahloujifar
January 2021
-
The Connection between Out-of-Distribution Generalization and Privacy of ML Models
Divyat Mahajan, Shruti Tople, Amit Sharma
Privacy Preserving Machine Learning (PriML/PPML) Workshop at NeurIPS’20 | December 2020
-
Secure Medical Image Analysis with CrypTFlow
Javier Alvarez-Valle, Pratik Bhatu, Nishanth Chandran, Divya Gupta, Aditya Nori, Aseem Rastogi, Mayank Rathee, Rahul Sharma, Shubham Ugare
NeurIPS PPML Workshop | December 2020
-
Analyzing Information Leakage of Updates to Natural Language Models
Santiago Zanella-Béguelin, Lukas Wutschitz, Shruti Tople, Victor Ruehle, Andrew Paverd, Olga Ohrimenko, Boris Köpf, Marc Brockschmidt
ACM Conference on Computer and Communication Security (CCS) | November 2020
-
Privately Learning Markov Random Fields
Huanyu Zhang, Gautam Kamath, Janardhan (Jana) Kulkarni, Zhiwei Steven Wu
ICML 2020 | July 2020
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Alleviating Privacy Attacks via Causal Learning
Shruti Tople, Amit Sharma, Aditya V. Nori
International Conference on Machine Learning (ICML) | July 2020
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DPImageBench: A Unified Benchmark for Differentially Private Image Synthesis
Chen Gong, Kecen Li, Zinan Lin, Tianhao Wang
CCS 2025 | July 2025
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The Canary’s Echo: Auditing Privacy Risks of LLM-Generated Synthetic Text
Matthieu Meeus, Lukas Wutschitz, Santiago Zanella-Béguelin, Reza Shokri, Shruti Tople
February 2025
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Differentially Private Synthetic Data via APIs 3: Using Simulators Instead of Foundation Models
Zinan Lin, Tadas Baltrusaitis, Sergey Yekhanin
February 2025
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Inferentially-Private Private Information
Shuaiqi Wang, Shuran Zheng, Zinan Lin, Giulia Fanti, Zhiwei Steven Wu
WWW 2025 | February 2025
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MMDT: Decoding the Trustworthiness and Safety of Multimodal Foundation Models
Chejian Xu, Jiawei Zhang, Zhaorun Chen, Chulin Xie, Mintong Kang, Zhuowen Yuan, Zidi Xiong, Chenhui Zhang, Lingzhi Yuan, Yi Zeng, Peiyang Xu, Chengquan Guo, Andy Zhou, Jeffrey Ziwei Tan, Zhun Wang, Alexander Xiong, Xuandong Zhao, Yu Gai, Francesco Pinto, Yujin Potter, Zhen Xiang, Zinan Lin, Dan Hendrycks, Dawn Song, Bo Li
ICLR 2025 | February 2025
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Closed-Form Bounds for DP-SGD against Record-level Inference
Giovanni Cherubin, Boris Köpf, Andrew Paverd, Shruti Tople, Lukas Wutschitz, Santiago Zanella-Béguelin
USENIX Security Symposium | August 2024
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Differentially Private Synthetic Data via Foundation Model APIs 2: Text
Chulin Xie, Zinan Lin, Arturs Backurs, Sivakanth Gopi, Da Yu, Huseyin Inan, Harsha Nori, Haotian Jiang, Huishuai Zhang, Yin Tat Lee, Bo Li, Sergey Yekhanin
ICML 2024 | July 2024
Spotlight
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Statistic Maximal Leakage
Shuaiqi Wang, Zinan Lin, Giulia Fanti
IEEE International Symposium on Information Theory 2024 | July 2024
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Dataset and Lessons Learned from the 2024 SaTML LLM Capture-the-Flag Competition
Edoardo Debenedetti, Javier Rando, Daniel Paleka, Fineas Silaghi, Dragos Albastroiu, Niv Cohen, Yuval Lemberg, Reshmi Ghosh, Ahmed Salem, Rui Wen, Giovanni Cherubin, Santiago Zanella-Béguelin, Robin Schmid, Victor Klemm, Takahiro Miki, Chenhao Li, Stefan Kraft, Mario Fritz, Florian Tramer, Sahar Abdelnabi, Lea Schönherr
NeurIPS 2024 | June 2024
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Privacy-Preserving In-Context Learning with Differentially Private Few-Shot Generation
Xinyu Tang, Richard Shin, Huseyin Inan, Andre Manoel, Fatemehsadat Mireshghallah, Zinan Lin, Sivakanth Gopi, Janardhan (Jana) Kulkarni, Robert Sim
ICLR 2024 | May 2024
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Differentially Private Synthetic Data via Foundation Model APIs 1: Images
Zinan Lin, Sivakanth Gopi, Janardhan (Jana) Kulkarni, Harsha Nori, Sergey Yekhanin
ICLR 2024 | May 2024
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Invariant Aggregator for Defending against Federated Backdoor Attacks
Xiaoya Wang, Dimitrios Dimitriadis, Oluwasanmi Koyejo, Shruti Tople
International Conference on Artificial Intelligence and Statistics, AISTATS | May 2024
編集者: Sanjoy Dasgupta, Stephan Mandt, Yingzhen Li
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Selective Pre-training for Private Fine-tuning
Da Yu, Sivakanth Gopi, Janardhan (Jana) Kulkarni, Zinan Lin, Saurabh Naik, Tomasz Lukasz Religa, Jian Yin, Huishuai Zhang
TMLR | May 2024
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Summary Statistic Privacy in Data Sharing
Zinan Lin, Shuaiqi Wang, Vyas Sekar, Giulia Fanti
IEEE Journal on Selected Areas in Information Theory | March 2024
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Advancing differential privacy: where we are now and future directions for real-world deployment
Rachel Cummings, Damien Desfontaines, David Evans, Roxana Geambasu, Yangsibo Huang, Matthew Jagielski, Peter Kairouz, Gautam Kamath, Sewoong Oh, Olga Ohrimenko, Nicolas Papernot, Ryan Rogers, Milan Shen, Shuang Song, Weijie Su, Andreas Terzis, Abhradeep Thakurta, Sergei Vassilvitskii, Yu-Xiang Wang, Li Xiong, Da Yu, Sergey Yekhanin, Huanyu Zhang, Wanrong Zhang
Harvard Data Science Review | January 2024, 巻6(1)
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Secure Floating-Point Training
Deevashwer Rathee, Anwesh Bhattacharya, Divya Gupta, Rahul Sharma, Dawn Song
Usenix Security Symposium 2023 | August 2023
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Confidential Computing within an AI Accelerator
Kapil Vaswani, Stavros Volos, Cédric Fournet, Antonio Nino Diaz, Ken Gordon, Balaji Vembu, Sam Webster, David Chisnall, Saurabh Kulkarni, Graham Cunningham , Richard Osborne , Daniel Wilkinson
2023 USENIX Annual Technical Conference | July 2023
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Bayesian Estimation of Differential Privacy
Santiago Zanella-Béguelin, Lukas Wutschitz, Shruti Tople, Ahmed Salem, Victor Ruehle, Andrew Paverd, Mohammad Naseri, Boris Köpf, Daniel Jones
2023 International Conference on Machine Learning | July 2023
編集者: Barbara Engelhardt, Emma Brunskill, Kyunghyun Cho
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DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Models
Boxin Wang, Weixin Chen, Hengzhi Pei, Chulin Xie, Mintong Kang, Chenhui Zhang, Chejian Xu, Zidi Xiong, Ritik Dutta, Rylan Schaeffer, Sang T. Truong, Simran Arora, Mantas Mazeika, Dan Hendrycks, Zinan Lin, Yu Cheng, Sanmi Koyejo, Dawn Song, Bo Li
NeurIPS 2023 | June 2023
Outstanding Paper Award
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Analyzing Leakage of Personally Identifiable Information in Language Models
Nils Lukas, Ahmed Salem, Robert Sim, Shruti Tople, Lukas Wutschitz, Santiago Zanella-Béguelin
2023 IEEE Symposium on Security and Privacy | May 2023
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SoK: Let the Privacy Games Begin! A Unified Treatment of Data Inference Privacy in Machine Learning
Ahmed Salem, Giovanni Cherubin, David Evans, Boris Köpf, Andrew Paverd, Anshuman Suri, Shruti Tople, Santiago Zanella-Béguelin
2023 IEEE Symposium on Security and Privacy | May 2023
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Exploring the Limits of Differentially Private Deep Learning with Group-wise Clipping
Jiyan He, Xuechen Li, Da Yu, Huishuai Zhang, Janardhan (Jana) Kulkarni, Yin Tat Lee, Arturs Backurs, Nenghai Yu, Jiang Bian
International Conference on Learning Representations (ICLR) 2023 | May 2023
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Membership Inference Attacks and Generalization: A Causal Perspective
Teodora Baluta, Shiqi Shen, S. Hitarth, Shruti Tople, Prateek Saxena
Computer and Communications Security (CCS) | November 2022
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SIMC: ML Inference Secure Against Malicious Clients at Semi-Honest Cost
Nishanth Chandran, Divya Gupta, Sai Lakshmi Bhavana Obbattu, Akash Shah
USENIX Security | August 2022
USENIX Security 2022
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dp-transformers: Training transformer models with differential privacy
Huseyin Inan, Andre Manoel, Lukas Wutschitz
July 2022
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Private Convex Optimization in General Norms
Sivakanth Gopi, Yin Tat Lee, Daogao Liu, Rouqi Shen, Kevin Tian
SODA 2023 | July 2022
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Circuit-PSI with Linear Complexity via Relaxed Batch OPPRF
Nishanth Chandran, Divya Gupta, Akash Shah
22nd Privacy Enhancing Technologies Symposium (PETS 2022) | June 2022
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SECFLOAT: Accurate Floating-Point meets Secure 2-Party Computation
Deevashwer Rathee, Anwesh Bhattacharya, Rahul Sharma, Divya Gupta, Nishanth Chandran, Aseem Rastogi
43rd IEEE Symposium on Security and Privacy (IEEE S&P 2022) | May 2022
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FLUTE: A Scalable, Extensible Framework for High-Performance Federated Learning Simulations
Dimitrios Dimitriadis, Mirian Hipolito Garcia, Daniel Madrigal, Andre Manoel, Robert Sim
March 2022
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Private Convex Optimization via Exponential Mechanism
Sivakanth Gopi, Yin Tat Lee, Daogao Liu
COLT 2022 | March 2022
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Secure Featurization and Applications to Secure Phishing Detection
Akash Shah, Nishanth Chandran, Divya Gupta, Mesfin Dema, Arun Gururajan, Huan Yu
CCSW 2021: The ACM Cloud Computing Security Workshop | October 2021
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Leakage of Dataset Properties in Multi-Party Machine Learning
Wanrong Zhang, Shruti Tople, Olga Ohrimenko
Usenix Security | August 2021
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Grey-box Extraction of Natural Language Models
Santiago Zanella-Béguelin, Shruti Tople, Andrew Paverd, Boris Köpf
International Conference on Machine Learning | July 2021
編集者: Marina Meila and Tong Zhang
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Membership Inference on Word Embedding and Beyond
Saeed Mahloujifar, Huseyin A. Inan, Melissa Chase, Esha Ghosh, Marcello Hasegawa
June 2021
-
Numerical Composition of Differential Privacy
Sivakanth Gopi, Yin Tat Lee, Lukas Wutschitz
NeurIPS 2021 | June 2021
Spotlight at NeurIPS 2021.
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Privacy Regularization: Joint Privacy-Utility Optimization in Language Models
Fatemehsadat Mireshghallah, Huseyin Inan, Marcello Hasegawa, Victor Rühle, Taylor Berg-Kirkpatrick, Robert Sim
2021 Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL 2021) | June 2021
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On Privacy and Confidentiality of Communications in Organizational Graphs
Masoumeh Shafieinejad, Huseyin A. Inan (huinan), Marcello Hasegawa, Robert Sim
May 2021
-
Causally Constrained Data Synthesis for Private Data Release
Varun Chandrasekaran, Darren Edge, Somesh Jha, Amit Sharma, Cheng Zhang, Shruti Tople
Distributed and Private Machine Learning (DPML) Workshop at ICLR’21 | May 2021
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Efficient Linear Multiparty PSI and Extensions to Circuit/Quorum PSI
Nishanth Chandran, Nishka Dasgupta, Divya Gupta, Sai Lakshmi Bhavana Obbattu, Sruthi Sekar, Akash Shah
28th Annual Conference on Computer and Communications Security (ACM CCS 2021) | May 2021
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Private Non-smooth Empirical Risk Minimization and Stochastic Convex Optimization in Subquadratic Steps
Janardhan (Jana) Kulkarni, Yin Tat Lee, Daogao Liu
NeurIPS 2021 | March 2021
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Training Data Leakage Analysis in Language Models
Huseyin A. Inan, Osman Ramadan, Lukas Wutschitz, Daniel Jones, Victor Rühle, James Withers, Robert Sim
February 2021
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SIRNN: A Math Library for Secure RNN Inference
Deevashwer Rathee, Mayank Rathee, Rahul Kranti Kiran Goli, Divya Gupta, Rahul Sharma, Nishanth Chandran, Aseem Rastogi
42nd IEEE Symposium on Security and Privacy (IEEE S&P 2021) | February 2021
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Fast and Memory Efficient Differentially Private-SGD via JL Projections
Zhiqi Bu, Sivakanth Gopi, Janardhan (Jana) Kulkarni, Yin Tat Lee, Judy Hanwen Shen, Uthaipon Tantipongpipat
NeurIPS 2021 | February 2021
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Property Inference from Poisoning
Melissa Chase, Esha Ghosh, Saeed Mahloujifar
January 2021
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Function Secret Sharing for Mixed-Mode and Fixed-Point Secure Computation
Elette Boyle, Nishanth Chandran, Niv Gilboa, Divya Gupta, Yuval Ishai, Nishant Kumar, Mayank Rathee
EUROCRYPT 2021 | January 2021
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The Connection between Out-of-Distribution Generalization and Privacy of ML Models
Divyat Mahajan, Shruti Tople, Amit Sharma
Privacy Preserving Machine Learning (PriML/PPML) Workshop at NeurIPS’20 | December 2020
-
Secure Medical Image Analysis with CrypTFlow
Javier Alvarez-Valle, Pratik Bhatu, Nishanth Chandran, Divya Gupta, Aditya Nori, Aseem Rastogi, Mayank Rathee, Rahul Sharma, Shubham Ugare
NeurIPS PPML Workshop | December 2020
-
Analyzing Information Leakage of Updates to Natural Language Models
Santiago Zanella-Béguelin, Lukas Wutschitz, Shruti Tople, Victor Ruehle, Andrew Paverd, Olga Ohrimenko, Boris Köpf, Marc Brockschmidt
ACM Conference on Computer and Communication Security (CCS) | November 2020
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An Empirical Study on the Intrinsic Privacy of SGD
Stephanie Hyland, Shruti Tople
Theory and Practice of Differential Privacy (CCS Worshop) | November 2020
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CrypTFlow2: Practical 2-Party Secure Inference
Deevashwer Rathee, Mayank Rathee, Nishant Kumar, Nishanth Chandran, Divya Gupta, Aseem Rastogi, Rahul Sharma
27th Annual Conference on Computer and Communications Security (ACM CCS 2020) | August 2020
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Visor: Privacy-Preserving Video Analytics as a Cloud Service
Rishabh Poddar, Ganesh Ananthanarayanan, Srinath Setty, Stavros Volos, Raluca Ada Popa
29th USENIX Security Symposium | August 2020
CSAW 2020 Applied Research Competition Award (Runner-up)
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Privately Learning Markov Random Fields
Huanyu Zhang, Gautam Kamath, Janardhan (Jana) Kulkarni, Zhiwei Steven Wu
ICML 2020 | July 2020
-
Alleviating Privacy Attacks via Causal Learning
Shruti Tople, Amit Sharma, Aditya V. Nori
International Conference on Machine Learning (ICML) | July 2020
-
Locally Private Hypothesis Selection
Sivakanth Gopi, Gautam Kamath, Janardhan (Jana) Kulkarni, Aleksandar Nikolov, Zhiwei Steven Wu, Huanyu Zhang
Conference on Learning Theory (COLT) 2020 | July 2020
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Differentially Private Set Union
Sivakanth Gopi, Pankaj Gulhane, Janardhan (Jana) Kulkarni, Judy Hanwen Shen, Milad Shokouhi, Sergey Yekhanin
International Conference on Machine Learning 2020 | July 2020
Contributed talk at TPDP 2020. Also appeared in the Journal of Privacy and Confidentiality (JPC).
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CrypTFlow: Secure TensorFlow Inference
Nishant Kumar, Mayank Rathee, Nishanth Chandran, Divya Gupta, Aseem Rastogi, Rahul Sharma
IEEE Symposium on Security and Privacy | May 2020
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Locally Private Gaussian Estimation
Matthew Joseph, Janardhan (Jana) Kulkarni, Jieming Mao, Zhiwei Steven Wu
November 2019
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An Algorithmic Framework For Differentially Private Data Analysis on Trusted Processors
Joshua Allen, Bolin Ding, Janardhan (Jana) Kulkarni, Harsha Nori, Olga Ohrimenko, Sergey Yekhanin
NeurIPS 2019 | August 2019
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EzPC: Programmable, Efficient, and Scalable Secure Two-Party Computation for Machine Learning
Nishanth Chandran, Divya Gupta, Aseem Rastogi, Rahul Sharma, Shardul Tripathi
IEEE European Symposium on Security and Privacy | February 2019
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SecureNN: Efficient and Private Neural Network Training
Sameer Wagh, Divya Gupta, Nishanth Chandran
Privacy Enhancing Technologies Symposium | February 2019
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Graviton: Trusted Execution Environments on GPUs
Stavros Volos, Kapil Vaswani, Rordigo Bruno
13th USENIX Symposium on Operating Systems Design and Implementation | October 2018
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Collecting Telemetry Data Privately
Bolin Ding, Janardhan (Jana) Kulkarni, Sergey Yekhanin
Advances in Neural Information Processing Systems 30 | December 2017
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Oblivious Multi-Party Machine Learning on Trusted Processors
Olya Ohrimenko, Felix Schuster, Cédric Fournet, Aastha Mehta, Sebastian Nowozin, Kapil Vaswani, Manuel Costa
The 25th USENIX Security Symposium 2016 | July 2016
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VC3: Trustworthy Data Analytics in the Cloud using SGX
Felix Schuster, Manuel Costa, Cédric Fournet, Christos Gkantsidis, Marcus Peinado, Gloria Mainar-Ruiz, Mark Russinovich
36th IEEE Symposium on Security and Privacy | May 2015
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MMDT: Decoding the Trustworthiness and Safety of Multimodal Foundation Models
Chejian Xu, Jiawei Zhang, Zhaorun Chen, Chulin Xie, Mintong Kang, Zhuowen Yuan, Zidi Xiong, Chenhui Zhang, Lingzhi Yuan, Yi Zeng, Peiyang Xu, Chengquan Guo, Andy Zhou, Jeffrey Ziwei Tan, Zhun Wang, Alexander Xiong, Xuandong Zhao, Yu Gai, Francesco Pinto, Yujin Potter, Zhen Xiang, Zinan Lin, Dan Hendrycks, Dawn Song, Bo Li
ICLR 2025 | February 2025
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Visor: Privacy-Preserving Video Analytics as a Cloud Service
Rishabh Poddar, Ganesh Ananthanarayanan, Srinath Setty, Stavros Volos, Raluca Ada Popa
29th USENIX Security Symposium | August 2020
CSAW 2020 Applied Research Competition Award (Runner-up)
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Differentially Private Synthetic Data via Foundation Model APIs 2: Text
Chulin Xie, Zinan Lin, Arturs Backurs, Sivakanth Gopi, Da Yu, Huseyin Inan, Harsha Nori, Haotian Jiang, Huishuai Zhang, Yin Tat Lee, Bo Li, Sergey Yekhanin
ICML 2024 | July 2024
Spotlight
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Differentially Private Synthetic Data via Foundation Model APIs 1: Images
Zinan Lin, Sivakanth Gopi, Janardhan (Jana) Kulkarni, Harsha Nori, Sergey Yekhanin
ICLR 2024 | May 2024
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Selective Pre-training for Private Fine-tuning
Da Yu, Sivakanth Gopi, Janardhan (Jana) Kulkarni, Zinan Lin, Saurabh Naik, Tomasz Lukasz Religa, Jian Yin, Huishuai Zhang
TMLR | May 2024
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Algorithmic Aspects of the Log-Laplace Transform and a Non-Euclidean Proximal Sampler
Sivakanth Gopi, Yin Tat Lee, Daogao Liu, Rouqi Shen, Kevin Tian
COLT 2023 | February 2023
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Differentially Private Model Compression
Fatemehsadat Mireshghallah, Arturs Backurs, Huseyin Inan, Lukas Wutschitz, Janardhan (Jana) Kulkarni
NeurIPS 2022 | November 2022
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When does differentially private learning not suffer in high dimensions?
Xuechen Li, Daogao Liu, Tatsunori Hashimoto, Huseyin Inan, Janardhan (Jana) Kulkarni, Yin Tat Lee, Abhradeep Guha Thakurta
NeurIPS’22 | November 2022
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Heterogeneous Ensemble Knowledge Transfer for Training Large Models in Federated Learning
Yae Jee Cho, Andre Manoel, Gauri Joshi, Robert Sim, Dimitrios Dimitriadis
IJCAI-ECAI’22 | August 2022
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Private Convex Optimization in General Norms
Sivakanth Gopi, Yin Tat Lee, Daogao Liu, Rouqi Shen, Kevin Tian
SODA 2023 | July 2022
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Differentially private fine-tuning of language models
Da Yu, Saurabh Naik, Arturs Backurs, Sivakanth Gopi, Huseyin Inan, Gautam Kamath, Janardhan (Jana) Kulkarni, Yin Tat Lee, Andre Manoel, Lukas Wutschitz, Sergey Yekhanin, Huishuai Zhang
ICLR 2022 | April 2022
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Private Convex Optimization via Exponential Mechanism
Sivakanth Gopi, Yin Tat Lee, Daogao Liu
COLT 2022 | March 2022
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Differentially private n-gram extraction
Kunho Kim, Sivakanth Gopi, Janardhan (Jana) Kulkarni, Sergey Yekhanin
NeurIPS 2021 | December 2021
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Tackling Dynamics in Federated Incremental Learning with Variational Embedding Rehearsal
Tae Jin Park, Kenichi Kumatani, Dimitrios Dimitriadis
October 2021
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Domain Generalization using Causal Matching
Divyat Mahajan, Shruti Tople, Amit Sharma
International Conference on Machine Learning (ICML) | July 2021
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Numerical Composition of Differential Privacy
Sivakanth Gopi, Yin Tat Lee, Lukas Wutschitz
NeurIPS 2021 | June 2021
Spotlight at NeurIPS 2021.
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Private Non-smooth Empirical Risk Minimization and Stochastic Convex Optimization in Subquadratic Steps
Janardhan (Jana) Kulkarni, Yin Tat Lee, Daogao Liu
NeurIPS 2021 | March 2021
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Differentially Private Correlation Clustering
Mark Bun, Marek Eliás, Janardhan (Jana) Kulkarni
2021 International Conference on Machine Learning | February 2021
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Fast and Memory Efficient Differentially Private-SGD via JL Projections
Zhiqi Bu, Sivakanth Gopi, Janardhan (Jana) Kulkarni, Yin Tat Lee, Judy Hanwen Shen, Uthaipon Tantipongpipat
NeurIPS 2021 | February 2021
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Privately Learning Markov Random Fields
Huanyu Zhang, Gautam Kamath, Janardhan (Jana) Kulkarni, Zhiwei Steven Wu
ICML 2020 | July 2020
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Locally Private Hypothesis Selection
Sivakanth Gopi, Gautam Kamath, Janardhan (Jana) Kulkarni, Aleksandar Nikolov, Zhiwei Steven Wu, Huanyu Zhang
Conference on Learning Theory (COLT) 2020 | July 2020
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Differentially Private Set Union
Sivakanth Gopi, Pankaj Gulhane, Janardhan (Jana) Kulkarni, Judy Hanwen Shen, Milad Shokouhi, Sergey Yekhanin
International Conference on Machine Learning 2020 | July 2020
Contributed talk at TPDP 2020. Also appeared in the Journal of Privacy and Confidentiality (JPC).
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Differentially private release of synthetic graphs
Marek Eliáš, Michael Kapralov, Janardhan (Jana) Kulkarni, Yin Tat Lee
2020 Symposium on Discrete Algorithms | January 2020
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Collecting Telemetry Data Privately
Bolin Ding, Janardhan (Jana) Kulkarni, Sergey Yekhanin
Advances in Neural Information Processing Systems 30 | December 2017
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Differentially Private Synthetic Data via Foundation Model APIs 2: Text
Chulin Xie, Zinan Lin, Arturs Backurs, Sivakanth Gopi, Da Yu, Huseyin Inan, Harsha Nori, Haotian Jiang, Huishuai Zhang, Yin Tat Lee, Bo Li, Sergey Yekhanin
ICML 2024 | July 2024
Spotlight
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Differentially Private Synthetic Data via Foundation Model APIs 1: Images
Zinan Lin, Sivakanth Gopi, Janardhan (Jana) Kulkarni, Harsha Nori, Sergey Yekhanin
ICLR 2024 | May 2024
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Advancing differential privacy: where we are now and future directions for real-world deployment
Rachel Cummings, Damien Desfontaines, David Evans, Roxana Geambasu, Yangsibo Huang, Matthew Jagielski, Peter Kairouz, Gautam Kamath, Sewoong Oh, Olga Ohrimenko, Nicolas Papernot, Ryan Rogers, Milan Shen, Shuang Song, Weijie Su, Andreas Terzis, Abhradeep Thakurta, Sergei Vassilvitskii, Yu-Xiang Wang, Li Xiong, Da Yu, Sergey Yekhanin, Huanyu Zhang, Wanrong Zhang
Harvard Data Science Review | January 2024, 巻6(1)
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Algorithmic Aspects of the Log-Laplace Transform and a Non-Euclidean Proximal Sampler
Sivakanth Gopi, Yin Tat Lee, Daogao Liu, Rouqi Shen, Kevin Tian
COLT 2023 | February 2023
-
Private Convex Optimization in General Norms
Sivakanth Gopi, Yin Tat Lee, Daogao Liu, Rouqi Shen, Kevin Tian
SODA 2023 | July 2022
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Private Convex Optimization via Exponential Mechanism
Sivakanth Gopi, Yin Tat Lee, Daogao Liu
COLT 2022 | March 2022
-
Numerical Composition of Differential Privacy
Sivakanth Gopi, Yin Tat Lee, Lukas Wutschitz
NeurIPS 2021 | June 2021
Spotlight at NeurIPS 2021.
-
Fast and Memory Efficient Differentially Private-SGD via JL Projections
Zhiqi Bu, Sivakanth Gopi, Janardhan (Jana) Kulkarni, Yin Tat Lee, Judy Hanwen Shen, Uthaipon Tantipongpipat
NeurIPS 2021 | February 2021
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Locally Private Hypothesis Selection
Sivakanth Gopi, Gautam Kamath, Janardhan (Jana) Kulkarni, Aleksandar Nikolov, Zhiwei Steven Wu, Huanyu Zhang
Conference on Learning Theory (COLT) 2020 | July 2020
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Differentially Private Set Union
Sivakanth Gopi, Pankaj Gulhane, Janardhan (Jana) Kulkarni, Judy Hanwen Shen, Milad Shokouhi, Sergey Yekhanin
International Conference on Machine Learning 2020 | July 2020
Contributed talk at TPDP 2020. Also appeared in the Journal of Privacy and Confidentiality (JPC).
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Confidential Computing within an AI Accelerator
Kapil Vaswani, Stavros Volos, Cédric Fournet, Antonio Nino Diaz, Ken Gordon, Balaji Vembu, Sam Webster, David Chisnall, Saurabh Kulkarni, Graham Cunningham , Richard Osborne , Daniel Wilkinson
2023 USENIX Annual Technical Conference | July 2023
-
Graviton: Trusted Execution Environments on GPUs
Stavros Volos, Kapil Vaswani, Rordigo Bruno
13th USENIX Symposium on Operating Systems Design and Implementation | October 2018
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Confidential Computing within an AI Accelerator
Kapil Vaswani, Stavros Volos, Cédric Fournet, Antonio Nino Diaz, Ken Gordon, Balaji Vembu, Sam Webster, David Chisnall, Saurabh Kulkarni, Graham Cunningham , Richard Osborne , Daniel Wilkinson
2023 USENIX Annual Technical Conference | July 2023
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Visor: Privacy-Preserving Video Analytics as a Cloud Service
Rishabh Poddar, Ganesh Ananthanarayanan, Srinath Setty, Stavros Volos, Raluca Ada Popa
29th USENIX Security Symposium | August 2020
CSAW 2020 Applied Research Competition Award (Runner-up)
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Graviton: Trusted Execution Environments on GPUs
Stavros Volos, Kapil Vaswani, Rordigo Bruno
13th USENIX Symposium on Operating Systems Design and Implementation | October 2018
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UserIdentifier: Implicit User Representations for Simple and Effective Personalized Sentiment Analysis
Fatemehsadat Mireshghallah, Vaishnavi Shrivastava, Milad Shokouhi, Taylor Berg-Kirkpatrick, Robert Sim, Dimitrios Dimitriadis
2022 Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL’22) | July 2022
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Membership Inference on Word Embedding and Beyond
Saeed Mahloujifar, Huseyin A. Inan, Melissa Chase, Esha Ghosh, Marcello Hasegawa
June 2021
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Privacy Regularization: Joint Privacy-Utility Optimization in Language Models
Fatemehsadat Mireshghallah, Huseyin Inan, Marcello Hasegawa, Victor Rühle, Taylor Berg-Kirkpatrick, Robert Sim
2021 Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL 2021) | June 2021
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On Privacy and Confidentiality of Communications in Organizational Graphs
Masoumeh Shafieinejad, Huseyin A. Inan (huinan), Marcello Hasegawa, Robert Sim
May 2021
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Training Data Leakage Analysis in Language Models
Huseyin A. Inan, Osman Ramadan, Lukas Wutschitz, Daniel Jones, Victor Rühle, James Withers, Robert Sim
February 2021
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Secure Medical Image Analysis with CrypTFlow
Javier Alvarez-Valle, Pratik Bhatu, Nishanth Chandran, Divya Gupta, Aditya Nori, Aseem Rastogi, Mayank Rathee, Rahul Sharma, Shubham Ugare
NeurIPS PPML Workshop | December 2020
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Secure Medical Image Analysis with CrypTFlow
Javier Alvarez-Valle, Pratik Bhatu, Nishanth Chandran, Divya Gupta, Aditya Nori, Aseem Rastogi, Mayank Rathee, Rahul Sharma, Shubham Ugare
NeurIPS PPML Workshop | December 2020
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EzPC: Programmable, Efficient, and Scalable Secure Two-Party Computation for Machine Learning
Nishanth Chandran, Divya Gupta, Aseem Rastogi, Rahul Sharma, Shardul Tripathi
IEEE European Symposium on Security and Privacy | February 2019
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Collecting Telemetry Data Privately
Bolin Ding, Janardhan (Jana) Kulkarni, Sergey Yekhanin
Advances in Neural Information Processing Systems 30 | December 2017
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DPImageBench: A Unified Benchmark for Differentially Private Image Synthesis
Chen Gong, Kecen Li, Zinan Lin, Tianhao Wang
CCS 2025 | July 2025
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Inferentially-Private Private Information
Shuaiqi Wang, Shuran Zheng, Zinan Lin, Giulia Fanti, Zhiwei Steven Wu
WWW 2025 | February 2025
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MMDT: Decoding the Trustworthiness and Safety of Multimodal Foundation Models
Chejian Xu, Jiawei Zhang, Zhaorun Chen, Chulin Xie, Mintong Kang, Zhuowen Yuan, Zidi Xiong, Chenhui Zhang, Lingzhi Yuan, Yi Zeng, Peiyang Xu, Chengquan Guo, Andy Zhou, Jeffrey Ziwei Tan, Zhun Wang, Alexander Xiong, Xuandong Zhao, Yu Gai, Francesco Pinto, Yujin Potter, Zhen Xiang, Zinan Lin, Dan Hendrycks, Dawn Song, Bo Li
ICLR 2025 | February 2025
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Closed-Form Bounds for DP-SGD against Record-level Inference
Giovanni Cherubin, Boris Köpf, Andrew Paverd, Shruti Tople, Lukas Wutschitz, Santiago Zanella-Béguelin
USENIX Security Symposium | August 2024
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Differentially Private Synthetic Data via Foundation Model APIs 2: Text
Chulin Xie, Zinan Lin, Arturs Backurs, Sivakanth Gopi, Da Yu, Huseyin Inan, Harsha Nori, Haotian Jiang, Huishuai Zhang, Yin Tat Lee, Bo Li, Sergey Yekhanin
ICML 2024 | July 2024
Spotlight
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Statistic Maximal Leakage
Shuaiqi Wang, Zinan Lin, Giulia Fanti
IEEE International Symposium on Information Theory 2024 | July 2024
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Dataset and Lessons Learned from the 2024 SaTML LLM Capture-the-Flag Competition
Edoardo Debenedetti, Javier Rando, Daniel Paleka, Fineas Silaghi, Dragos Albastroiu, Niv Cohen, Yuval Lemberg, Reshmi Ghosh, Ahmed Salem, Rui Wen, Giovanni Cherubin, Santiago Zanella-Béguelin, Robin Schmid, Victor Klemm, Takahiro Miki, Chenhao Li, Stefan Kraft, Mario Fritz, Florian Tramer, Sahar Abdelnabi, Lea Schönherr
NeurIPS 2024 | June 2024
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Privacy-Preserving In-Context Learning with Differentially Private Few-Shot Generation
Xinyu Tang, Richard Shin, Huseyin Inan, Andre Manoel, Fatemehsadat Mireshghallah, Zinan Lin, Sivakanth Gopi, Janardhan (Jana) Kulkarni, Robert Sim
ICLR 2024 | May 2024
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Differentially Private Synthetic Data via Foundation Model APIs 1: Images
Zinan Lin, Sivakanth Gopi, Janardhan (Jana) Kulkarni, Harsha Nori, Sergey Yekhanin
ICLR 2024 | May 2024
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Invariant Aggregator for Defending against Federated Backdoor Attacks
Xiaoya Wang, Dimitrios Dimitriadis, Oluwasanmi Koyejo, Shruti Tople
International Conference on Artificial Intelligence and Statistics, AISTATS | May 2024
編集者: Sanjoy Dasgupta, Stephan Mandt, Yingzhen Li
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Training Private and Efficient Language Models with Synthetic Data from LLMs
Da Yu, Arturs Backurs, Sivakanth Gopi, Huseyin Inan, Janardhan (Jana) Kulkarni, Zinan Lin, Chulin Xie, Huishuai Zhang, Wanrong Zhang
NeurIPS 2023 SoLaR Workshop | December 2023
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Secure Floating-Point Training
Deevashwer Rathee, Anwesh Bhattacharya, Divya Gupta, Rahul Sharma, Dawn Song
Usenix Security Symposium 2023 | August 2023
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Confidential Computing within an AI Accelerator
Kapil Vaswani, Stavros Volos, Cédric Fournet, Antonio Nino Diaz, Ken Gordon, Balaji Vembu, Sam Webster, David Chisnall, Saurabh Kulkarni, Graham Cunningham , Richard Osborne , Daniel Wilkinson
2023 USENIX Annual Technical Conference | July 2023
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Bayesian Estimation of Differential Privacy
Santiago Zanella-Béguelin, Lukas Wutschitz, Shruti Tople, Ahmed Salem, Victor Ruehle, Andrew Paverd, Mohammad Naseri, Boris Köpf, Daniel Jones
2023 International Conference on Machine Learning | July 2023
編集者: Barbara Engelhardt, Emma Brunskill, Kyunghyun Cho
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DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Models
Boxin Wang, Weixin Chen, Hengzhi Pei, Chulin Xie, Mintong Kang, Chenhui Zhang, Chejian Xu, Zidi Xiong, Ritik Dutta, Rylan Schaeffer, Sang T. Truong, Simran Arora, Mantas Mazeika, Dan Hendrycks, Zinan Lin, Yu Cheng, Sanmi Koyejo, Dawn Song, Bo Li
NeurIPS 2023 | June 2023
Outstanding Paper Award
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Analyzing Leakage of Personally Identifiable Information in Language Models
Nils Lukas, Ahmed Salem, Robert Sim, Shruti Tople, Lukas Wutschitz, Santiago Zanella-Béguelin
2023 IEEE Symposium on Security and Privacy | May 2023
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SoK: Let the Privacy Games Begin! A Unified Treatment of Data Inference Privacy in Machine Learning
Ahmed Salem, Giovanni Cherubin, David Evans, Boris Köpf, Andrew Paverd, Anshuman Suri, Shruti Tople, Santiago Zanella-Béguelin
2023 IEEE Symposium on Security and Privacy | May 2023
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Exploring the Limits of Differentially Private Deep Learning with Group-wise Clipping
Jiyan He, Xuechen Li, Da Yu, Huishuai Zhang, Janardhan (Jana) Kulkarni, Yin Tat Lee, Arturs Backurs, Nenghai Yu, Jiang Bian
International Conference on Learning Representations (ICLR) 2023 | May 2023
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Algorithmic Aspects of the Log-Laplace Transform and a Non-Euclidean Proximal Sampler
Sivakanth Gopi, Yin Tat Lee, Daogao Liu, Rouqi Shen, Kevin Tian
COLT 2023 | February 2023
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Differentially Private Model Compression
Fatemehsadat Mireshghallah, Arturs Backurs, Huseyin Inan, Lukas Wutschitz, Janardhan (Jana) Kulkarni
NeurIPS 2022 | November 2022
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When does differentially private learning not suffer in high dimensions?
Xuechen Li, Daogao Liu, Tatsunori Hashimoto, Huseyin Inan, Janardhan (Jana) Kulkarni, Yin Tat Lee, Abhradeep Guha Thakurta
NeurIPS’22 | November 2022
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Membership Inference Attacks and Generalization: A Causal Perspective
Teodora Baluta, Shiqi Shen, S. Hitarth, Shruti Tople, Prateek Saxena
Computer and Communications Security (CCS) | November 2022
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Heterogeneous Ensemble Knowledge Transfer for Training Large Models in Federated Learning
Yae Jee Cho, Andre Manoel, Gauri Joshi, Robert Sim, Dimitrios Dimitriadis
IJCAI-ECAI’22 | August 2022
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SIMC: ML Inference Secure Against Malicious Clients at Semi-Honest Cost
Nishanth Chandran, Divya Gupta, Sai Lakshmi Bhavana Obbattu, Akash Shah
USENIX Security | August 2022
USENIX Security 2022
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UserIdentifier: Implicit User Representations for Simple and Effective Personalized Sentiment Analysis
Fatemehsadat Mireshghallah, Vaishnavi Shrivastava, Milad Shokouhi, Taylor Berg-Kirkpatrick, Robert Sim, Dimitrios Dimitriadis
2022 Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL’22) | July 2022
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Private Convex Optimization in General Norms
Sivakanth Gopi, Yin Tat Lee, Daogao Liu, Rouqi Shen, Kevin Tian
SODA 2023 | July 2022
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Circuit-PSI with Linear Complexity via Relaxed Batch OPPRF
Nishanth Chandran, Divya Gupta, Akash Shah
22nd Privacy Enhancing Technologies Symposium (PETS 2022) | June 2022
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SECFLOAT: Accurate Floating-Point meets Secure 2-Party Computation
Deevashwer Rathee, Anwesh Bhattacharya, Rahul Sharma, Divya Gupta, Nishanth Chandran, Aseem Rastogi
43rd IEEE Symposium on Security and Privacy (IEEE S&P 2022) | May 2022
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Differentially private fine-tuning of language models
Da Yu, Saurabh Naik, Arturs Backurs, Sivakanth Gopi, Huseyin Inan, Gautam Kamath, Janardhan (Jana) Kulkarni, Yin Tat Lee, Andre Manoel, Lukas Wutschitz, Sergey Yekhanin, Huishuai Zhang
ICLR 2022 | April 2022
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Private Convex Optimization via Exponential Mechanism
Sivakanth Gopi, Yin Tat Lee, Daogao Liu
COLT 2022 | March 2022
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Differentially private n-gram extraction
Kunho Kim, Sivakanth Gopi, Janardhan (Jana) Kulkarni, Sergey Yekhanin
NeurIPS 2021 | December 2021
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Secure Featurization and Applications to Secure Phishing Detection
Akash Shah, Nishanth Chandran, Divya Gupta, Mesfin Dema, Arun Gururajan, Huan Yu
CCSW 2021: The ACM Cloud Computing Security Workshop | October 2021
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Membership Inference Attacks Against NLP Classification Models
Virat Shejwalkar, Huseyin Inan, Amir Houmansadr, Robert Sim
NeurIPS 2021 Workshop Privacy in Machine Learning | September 2021
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Leakage of Dataset Properties in Multi-Party Machine Learning
Wanrong Zhang, Shruti Tople, Olga Ohrimenko
Usenix Security | August 2021
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Accuracy, Interpretability, and Differential Privacy via Explainable Boosting
Harsha Nori, Rich Caruana, Zhiqi Bu, Judy Hanwen Shen, Janardhan (Jana) Kulkarni
2021 International Conference on Machine Learning | July 2021
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Domain Generalization using Causal Matching
Divyat Mahajan, Shruti Tople, Amit Sharma
International Conference on Machine Learning (ICML) | July 2021
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Grey-box Extraction of Natural Language Models
Santiago Zanella-Béguelin, Shruti Tople, Andrew Paverd, Boris Köpf
International Conference on Machine Learning | July 2021
編集者: Marina Meila and Tong Zhang
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Numerical Composition of Differential Privacy
Sivakanth Gopi, Yin Tat Lee, Lukas Wutschitz
NeurIPS 2021 | June 2021
Spotlight at NeurIPS 2021.
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Privacy Regularization: Joint Privacy-Utility Optimization in Language Models
Fatemehsadat Mireshghallah, Huseyin Inan, Marcello Hasegawa, Victor Rühle, Taylor Berg-Kirkpatrick, Robert Sim
2021 Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL 2021) | June 2021
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Causally Constrained Data Synthesis for Private Data Release
Varun Chandrasekaran, Darren Edge, Somesh Jha, Amit Sharma, Cheng Zhang, Shruti Tople
Distributed and Private Machine Learning (DPML) Workshop at ICLR’21 | May 2021
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Efficient Linear Multiparty PSI and Extensions to Circuit/Quorum PSI
Nishanth Chandran, Nishka Dasgupta, Divya Gupta, Sai Lakshmi Bhavana Obbattu, Sruthi Sekar, Akash Shah
28th Annual Conference on Computer and Communications Security (ACM CCS 2021) | May 2021
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Private Non-smooth Empirical Risk Minimization and Stochastic Convex Optimization in Subquadratic Steps
Janardhan (Jana) Kulkarni, Yin Tat Lee, Daogao Liu
NeurIPS 2021 | March 2021
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Differentially Private Correlation Clustering
Mark Bun, Marek Eliás, Janardhan (Jana) Kulkarni
2021 International Conference on Machine Learning | February 2021
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SIRNN: A Math Library for Secure RNN Inference
Deevashwer Rathee, Mayank Rathee, Rahul Kranti Kiran Goli, Divya Gupta, Rahul Sharma, Nishanth Chandran, Aseem Rastogi
42nd IEEE Symposium on Security and Privacy (IEEE S&P 2021) | February 2021
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Fast and Memory Efficient Differentially Private-SGD via JL Projections
Zhiqi Bu, Sivakanth Gopi, Janardhan (Jana) Kulkarni, Yin Tat Lee, Judy Hanwen Shen, Uthaipon Tantipongpipat
NeurIPS 2021 | February 2021
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Function Secret Sharing for Mixed-Mode and Fixed-Point Secure Computation
Elette Boyle, Nishanth Chandran, Niv Gilboa, Divya Gupta, Yuval Ishai, Nishant Kumar, Mayank Rathee
EUROCRYPT 2021 | January 2021
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The Connection between Out-of-Distribution Generalization and Privacy of ML Models
Divyat Mahajan, Shruti Tople, Amit Sharma
Privacy Preserving Machine Learning (PriML/PPML) Workshop at NeurIPS’20 | December 2020
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Secure Medical Image Analysis with CrypTFlow
Javier Alvarez-Valle, Pratik Bhatu, Nishanth Chandran, Divya Gupta, Aditya Nori, Aseem Rastogi, Mayank Rathee, Rahul Sharma, Shubham Ugare
NeurIPS PPML Workshop | December 2020
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Analyzing Information Leakage of Updates to Natural Language Models
Santiago Zanella-Béguelin, Lukas Wutschitz, Shruti Tople, Victor Ruehle, Andrew Paverd, Olga Ohrimenko, Boris Köpf, Marc Brockschmidt
ACM Conference on Computer and Communication Security (CCS) | November 2020
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An Empirical Study on the Intrinsic Privacy of SGD
Stephanie Hyland, Shruti Tople
Theory and Practice of Differential Privacy (CCS Worshop) | November 2020
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CrypTFlow2: Practical 2-Party Secure Inference
Deevashwer Rathee, Mayank Rathee, Nishant Kumar, Nishanth Chandran, Divya Gupta, Aseem Rastogi, Rahul Sharma
27th Annual Conference on Computer and Communications Security (ACM CCS 2020) | August 2020
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Visor: Privacy-Preserving Video Analytics as a Cloud Service
Rishabh Poddar, Ganesh Ananthanarayanan, Srinath Setty, Stavros Volos, Raluca Ada Popa
29th USENIX Security Symposium | August 2020
CSAW 2020 Applied Research Competition Award (Runner-up)
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Privately Learning Markov Random Fields
Huanyu Zhang, Gautam Kamath, Janardhan (Jana) Kulkarni, Zhiwei Steven Wu
ICML 2020 | July 2020
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Alleviating Privacy Attacks via Causal Learning
Shruti Tople, Amit Sharma, Aditya V. Nori
International Conference on Machine Learning (ICML) | July 2020
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Locally Private Hypothesis Selection
Sivakanth Gopi, Gautam Kamath, Janardhan (Jana) Kulkarni, Aleksandar Nikolov, Zhiwei Steven Wu, Huanyu Zhang
Conference on Learning Theory (COLT) 2020 | July 2020
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Differentially Private Set Union
Sivakanth Gopi, Pankaj Gulhane, Janardhan (Jana) Kulkarni, Judy Hanwen Shen, Milad Shokouhi, Sergey Yekhanin
International Conference on Machine Learning 2020 | July 2020
Contributed talk at TPDP 2020. Also appeared in the Journal of Privacy and Confidentiality (JPC).
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CrypTFlow: Secure TensorFlow Inference
Nishant Kumar, Mayank Rathee, Nishanth Chandran, Divya Gupta, Aseem Rastogi, Rahul Sharma
IEEE Symposium on Security and Privacy | May 2020
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Differentially private release of synthetic graphs
Marek Eliáš, Michael Kapralov, Janardhan (Jana) Kulkarni, Yin Tat Lee
2020 Symposium on Discrete Algorithms | January 2020
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An Algorithmic Framework For Differentially Private Data Analysis on Trusted Processors
Joshua Allen, Bolin Ding, Janardhan (Jana) Kulkarni, Harsha Nori, Olga Ohrimenko, Sergey Yekhanin
NeurIPS 2019 | August 2019
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EzPC: Programmable, Efficient, and Scalable Secure Two-Party Computation for Machine Learning
Nishanth Chandran, Divya Gupta, Aseem Rastogi, Rahul Sharma, Shardul Tripathi
IEEE European Symposium on Security and Privacy | February 2019
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SecureNN: Efficient and Private Neural Network Training
Sameer Wagh, Divya Gupta, Nishanth Chandran
Privacy Enhancing Technologies Symposium | February 2019
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Graviton: Trusted Execution Environments on GPUs
Stavros Volos, Kapil Vaswani, Rordigo Bruno
13th USENIX Symposium on Operating Systems Design and Implementation | October 2018
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Collecting Telemetry Data Privately
Bolin Ding, Janardhan (Jana) Kulkarni, Sergey Yekhanin
Advances in Neural Information Processing Systems 30 | December 2017
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Oblivious Multi-Party Machine Learning on Trusted Processors
Olya Ohrimenko, Felix Schuster, Cédric Fournet, Aastha Mehta, Sebastian Nowozin, Kapil Vaswani, Manuel Costa
The 25th USENIX Security Symposium 2016 | July 2016
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VC3: Trustworthy Data Analytics in the Cloud using SGX
Felix Schuster, Manuel Costa, Cédric Fournet, Christos Gkantsidis, Marcus Peinado, Gloria Mainar-Ruiz, Mark Russinovich
36th IEEE Symposium on Security and Privacy | May 2015
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The Canary’s Echo: Auditing Privacy Risks of LLM-Generated Synthetic Text
Matthieu Meeus, Lukas Wutschitz, Santiago Zanella-Béguelin, Reza Shokri, Shruti Tople
February 2025
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Differentially Private Synthetic Data via APIs 3: Using Simulators Instead of Foundation Models
Zinan Lin, Tadas Baltrusaitis, Sergey Yekhanin
February 2025
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dp-transformers: Training transformer models with differential privacy
Huseyin Inan, Andre Manoel, Lukas Wutschitz
July 2022
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Membership Inference on Word Embedding and Beyond
Saeed Mahloujifar, Huseyin A. Inan, Melissa Chase, Esha Ghosh, Marcello Hasegawa
June 2021
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Training Data Leakage Analysis in Language Models
Huseyin A. Inan, Osman Ramadan, Lukas Wutschitz, Daniel Jones, Victor Rühle, James Withers, Robert Sim
February 2021
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Property Inference from Poisoning
Melissa Chase, Esha Ghosh, Saeed Mahloujifar
January 2021
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Project Florida: Federated Learning Made Easy
Daniel Madrigal, Andre Manoel, Jialei Chen, Nalin Singal, Robert Sim
| July 2023
Microsoft による投稿
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Selective Pre-training for Private Fine-tuning
Da Yu, Sivakanth Gopi, Janardhan (Jana) Kulkarni, Zinan Lin, Saurabh Naik, Tomasz Lukasz Religa, Jian Yin, Huishuai Zhang
TMLR | May 2024
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Summary Statistic Privacy in Data Sharing
Zinan Lin, Shuaiqi Wang, Vyas Sekar, Giulia Fanti
IEEE Journal on Selected Areas in Information Theory | March 2024
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Advancing differential privacy: where we are now and future directions for real-world deployment
Rachel Cummings, Damien Desfontaines, David Evans, Roxana Geambasu, Yangsibo Huang, Matthew Jagielski, Peter Kairouz, Gautam Kamath, Sewoong Oh, Olga Ohrimenko, Nicolas Papernot, Ryan Rogers, Milan Shen, Shuang Song, Weijie Su, Andreas Terzis, Abhradeep Thakurta, Sergei Vassilvitskii, Yu-Xiang Wang, Li Xiong, Da Yu, Sergey Yekhanin, Huanyu Zhang, Wanrong Zhang
Harvard Data Science Review | January 2024, 巻6(1)
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Personalized Federated Learning with Adaptive Batchnorm for Healthcare
Wang Lu, Jindong Wang, Yiqiang Chen, Xin Qin, Renjun Xu, Dimitrios Dimitriadis, Tao Qin
IEEE Transactions on Big Data | May 2022
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FLUTE: A Scalable, Extensible Framework for High-Performance Federated Learning Simulations
Dimitrios Dimitriadis, Mirian Hipolito Garcia, Daniel Madrigal, Andre Manoel, Robert Sim
March 2022
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Tackling Dynamics in Federated Incremental Learning with Variational Embedding Rehearsal
Tae Jin Park, Kenichi Kumatani, Dimitrios Dimitriadis
October 2021
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On Privacy and Confidentiality of Communications in Organizational Graphs
Masoumeh Shafieinejad, Huseyin A. Inan (huinan), Marcello Hasegawa, Robert Sim
May 2021
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Locally Private Gaussian Estimation
Matthew Joseph, Janardhan (Jana) Kulkarni, Jieming Mao, Zhiwei Steven Wu
November 2019
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