Abstract

We present Sunlight, a system that detects the causes of target-ing phenomena on the web – such as personalized advertisements, recommendations, or content – at large scale and with solid statisti-cal confidence. Today’s web is growing increasingly complex and impenetrable as myriad of services collect, analyze, use, and ex-change 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 confi-dence in the results, making them difficult to trust or interpret. Sunlight brings a principled and scalable methodology to per-sonal data measurements by adapting well-established methods from statistics for the specific problem of targeting detection. Our method-