The third-party online tracking ecosystem lacks transparency about (1) which companies track users, (2) what user data is being collected, (3) what technologies are being used for tracking, and (4) data flows between trackers. Automated measurement can enable transparency and has already resulted in greater privacy awareness, improved privacy tools, and, at times, regulatory enforcement actions.
At Princeton we have built OpenWPM, a platform for online tracking transparency. We have used it in several published studies to detect and reverse-engineer online tracking. We now aim to democratize web privacy measurement: transform it from a niche research field to a widely available tool. We will do this in two steps: use OpenWPM to publish a “web privacy census” — a monthly web-scale measurement of tracking and privacy, comprising 1 million sites. The census will detect and measure many or most of the types of known privacy violations reported by researchers so far: circumvention of cookie blocking, leakage of PII to third parties, canvas fingerprinting, and more. Second, we will build an analysis platform to allow anyone to analyze the census data with minimal expertise. The platform will have “1-click reproducibility’” which will allow packaging and distributing study data, scripts, and results in a format that’s easy to replicate and extend.