Blocking Tracking JavaScript at the Function Granularity

ACM SIGSAC Conference on Computer and Communications Security, (ACM CCS 2024)

Abdul Haddi Amjad, Shaoor Munir, Zubair Shafiq, Muhammad Ali Gulzar

Abstract

Modern websites extensively rely on JavaScript to implement both functionality and tracking. Existing privacy enhancing content blocking tools struggle against mixed scripts, which simultaneously implement both functionality and tracking, because blocking the script would break functionality and not blocking it would allow tracking. We propose Not.js, a fine grained JavaScript blocking tool that operates at the function level granularity. Not.js’s strengths lie in analyzing the dynamic execution context, including the call stack and calling context of each JavaScript function, and then encoding this context to build a rich graph representation. Not.js trains a supervised machine learning classifier on a webpage’s graph representation to first detect tracking at the JavaScript function level and then automatically generate surrogate scripts that preserve functionality while removing tracking. Our evaluation of Not.js on the top 10K websites demonstrates that it achieves high precision (94%) and recall (98%) in detecting tracking JavaScript functions, outperforming the state of the art while being robust against off the shelf JavaScript obfuscation. Fine grained detection of tracking functions allows Not.js to automatically generate surrogate scripts that remove tracking JavaScript functions without causing major breakage. Our deployment of Not.js shows that mixed scripts are present on 62.3% of the top 10K websites, with 70.6% of the mixed scripts being third party that engage in tracking activities such as cookie ghostwriting. We share a sample of the tracking functions detected by Not.js within mixed scripts not currently on filter lists with filter list authors, who confirm that these scripts are not blocked due to potential functionality breakage, despite being known to implement tracking.