Bootstrapping Privacy Compliance in Big Data Systems - Sen, Shayak; Guha, Saikat; Dutta, Anupam; Rajamani, Sriram; Tsai, Janice; Wing, Jeannette (Proceedings of the 35th IEEE Symposium on Security & Privacy (Oakland), 2014)
With the rapid increase in cloud services collecting and using user data to offer personalized experiences, ensuring that these services comply with their privacy policies has become a business imperative for building user trust. However, most compliance efforts in industry today rely on manual review processes and audits designed to safeguard user data, and therefore are resource intensive and lack coverage. In this paper, we present our experience building and operating a system to automate privacy policy compliance checking in Bing. Central to the design of the system are (a) L EGALEASE —a language that allows specification of privacy policies that impose restrictions on how user data is handled; and (b) G ROK —a data inventory for Map-Reduce-like big data systems that tracks how user data flows among programs. G ROK maps code-level schema elements to datatypes in LEGALEASE , in essence, annotating existing programs with information flow types with minimal human input. Compliance checking is thus reduced to information flow analysis of big data systems. The system, bootstrapped by a small team, checks compliance daily of millions of lines of ever-changing source code written by several thousand developers.