Sunlight: Fine-grained Targeting Detection at Scale with Statistical Confidence - Lecuyer, Mathias; Spahn, Riley; Spiliopoulos, Giannis; Chaintreau, Augustin; Geambasu, Roxana; Hsu, Daniel (CCS’15, 2015)
We present Sunlight, a system that detects the causes of targeting phenomena on the web – such as personalized advertisements, recommendations,orcontent–atlargescaleandwithsolidstatistical conﬁdence. Today’s web is growing increasingly complex and impenetrable as myriad of services collect, analyze, use, and exchange users’ personal information. No one can tell who has what data, for what purposes they are using it, and how those uses affect the users. The few studies that exist reveal problematic effects – such as discriminatory pricing and advertising – but they are either too small-scale to generalize or lack formal assessments of conﬁdence in the results, making them difﬁcult to trust or interpret. Sunlight brings a principled and scalable methodology to personal data measurements by adapting well-established methods from statistics for the speciﬁc problem of targeting detection. Our methodology formally separates different operations into four key phases: scalable hypothesis generation, interpretable hypothesis formation, statistical signiﬁcance testing, and multiple testing correction. Each phase bears instantiations from multiple mechanisms from statistics, each making different assumptions and tradeoffs. Sunlight offers a modular design that allows exploration of this vast design space. We explore a portion of this space, thoroughly evaluating the tradeoffs both analytically and experimentally. Our exploration reveals subtle tensions between scalability and conﬁdence. Sunlight’s default functioning strikes a balance to provide the ﬁrst system that can diagnose targeting at ﬁne granularity, at scale, and with solid statistical justiﬁcation of its results. We showcase our system by running two measurement studies of targeting on the web, both the largest of their kind. Our studies—about ad targeting in Gmail and on the web—reveal statistically justiﬁable evidence that contradicts two Google statements regarding the lack of targeting on sensitive and prohibited topics.