Muhammad Ahmad Bashir (Northeastern University), Umar Farooq (LUMS Pakistan), Maryam Shahid (LUMS Pakistan), Muhammad Fareed Zaffar (LUMS Pakistan), Christo Wilson (Northeastern University)

Widely reported privacy issues concerning major online advertising platforms (e.g., Facebook) have heightened concerns among users about the data that is collected about them. However, while we have a comprehensive understanding who collects data on users, as well as how tracking is implemented, there is still a significant gap in our understanding: what information do advertisers actually infer about users, and is this information accurate?

In this study, we leverage Ad Preference Managers (APMs) as a lens through which to address this gap. APMs are transparency tools offered by some advertising platforms that allow users to see the interest profiles that are constructed about them. We recruited 220 participants to install an IRB approved browser extension that collected their interest profiles from four APMs (Google, Facebook, Oracle BlueKai, and Neilsen eXelate), as well as behavioral and survey data. We use this data to analyze the size and correctness of interest profiles, compare their composition across the four platforms, and investigate the origins of the data underlying these profiles.

View More Papers

Stealthy Adversarial Perturbations Against Real-Time Video Classification Systems

Shasha Li (University of California Riverside), Ajaya Neupane (University of California Riverside), Sujoy Paul (University of California Riverside), Chengyu Song (University of California Riverside), Srikanth V. Krishnamurthy (University of California Riverside), Amit K. Roy Chowdhury (University of California Riverside), Ananthram Swami (United States Army Research Laboratory)

Read More

Constructing an Adversary Solver for Equihash

Xiaofei Bai (School of Computer Science, Fudan University), Jian Gao (School of Computer Science, Fudan University), Chenglong Hu (School of Computer Science, Fudan University), Liang Zhang (School of Computer Science, Fudan University)

Read More

RFDIDS: Radio Frequency-based Distributed Intrusion Detection System for the...

Tohid Shekari (ECE, Georgia Tech), Christian Bayens (ECE, Georgia Tech), Morris Cohen (ECE, Georgia Tech), Lukas Graber (ECE, Georgia Tech), Raheem Beyah (ECE, Georgia Tech)

Read More

Robust Performance Metrics for Authentication Systems

Shridatt Sugrim (Rutgers University), Can Liu (Rutgers University), Meghan McLean (Rutgers University), Janne Lindqvist (Rutgers University)

Read More