SINBAD: Saliency-informed detection of breakage caused by ad blocking
Saiid El Hajj Chehade, Sandra Siby, Carmela Troncoso
IEEE Symposium on Security and Privacy 2024 · Day 1 · Continental Ballroom 4
The proliferation of privacy-enhancing technologies (PETs) like ad blockers has dramatically improved user experience and privacy online. However, these tools often modify web page content and behavior, leading to unintended side effects known as "breakage." Breakage occurs when an ad blocker, in its effort to remove unwanted elements, inadvertently impairs the expected functionality of a web page, either partially or completely. This can manifest as missing content, non-interactive elements, or broken layouts, preventing users from properly engaging with the site. The talk "SINBAD: Saliency-informed detection of breakage caused by ad blocking" by Saiid El Hajj Chehade, Sandra Siby, and Carmela Troncoso introduces a novel, automated machine learning pipeline designed to proactively detect such breakage.
AI review
SINBAD presents a critical advancement in ad blocker efficacy, tackling the long-standing problem of dynamic breakage that plagues privacy tools. By integrating saliency modeling and simulated user interactions, this novel ML pipeline proactively detects functional impairments, a blind spot for all prior automated methods. This work directly improves user experience and significantly reduces the manual burden on ad blocker maintainers, making it a must-see for anyone in web privacy or automated testing.