Ben Weinshel, Miranda Wei, Mainack Mondal, Euirim Choi, Shawn Shan, Claire Dolin, Michelle L. Mazurek, Blase Ur
Proceedings of the 26th ACM Conference on Computer and Communications Security (CCS). London, UK, November 2019. (CCS 2019)
Abstract
Internet companies track users’ online activity to make inferences about their interests, which are then used to target ads and personalize their web experience. Prior work has shown that existing privacy-protective tools give users only a limited understanding and incomplete picture of online tracking. We present Tracking Transparency, a privacy-preserving browser extension that visualizes examples of long-term,longitudinal information that third-party trackers could have inferred from users’ browsing. The extension uses a client-side topic modeling algorithm to categorize pages that users visit and combines this with data about the web trackers encountered over time to create these visualizations. We conduct a longitudinal field study in which 425 participants use one of six variants of our extension for a week. We find that, after using the extension, participants have more accurate perceptions of the extent of tracking and also intend to take privacy-protecting actions.