Website Data Transparency in the Browser
Authors: Sebastian Zimmeck (Wesleyan University), Daniel Goldelman (Wesleyan University), Owen Kaplan (Wesleyan University), Logan Brown (Wesleyan University), Justin Casler (Wesleyan University), Judeley Jean-Charles (Wesleyan University), Joe Champeau (Wesleyan University), Hamza Harkous (Google)
Volume: 2024
Issue: 2
Pages: 211–234
DOI: https://doi.org/10.56553/popets-2024-0048
Abstract: Data collection by websites and their integrated third parties is often not transparent. We design privacy interfaces for the browser to help people understand who is collecting which data from them. In a proof of concept browser extension, Privacy Pioneer, we implement a privacy popup, a privacy history interface, and a watchlist to notify people when their data is collected. For detecting location data collection, we develop a machine learning model based on TinyBERT, which reaches an average F1 score of 0.94. We supplement our model with deterministic methods to detect trackers, collection of personal data, and other monetization techniques. In a usability study with 100 participants 82% found Privacy Pioneer easy to understand and 90% found it useful indicating the value of privacy interfaces directly integrated in the browser.
Keywords: Web Privacy, Data Transparency, Privacy Dashboards, Notice and Choice, Consent, Privacy Labels, Usability, Privacy Enhancing Technologies, Web Traffic Analysis, BERT, Machine Learning
Copyright in PoPETs articles are held by their authors. This article is published under a Creative Commons Attribution 4.0 license.
