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.

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Tranco: A Research-Oriented Top Sites Ranking Hardened Against Manipulation

Victor Le Pochat (imec-DistriNet, KU Leuven), Tom Van Goethem (imec-DistriNet, KU Leuven), Samaneh Tajalizadehkhoob (Delft University of Technology), Maciej Korczyński (Grenoble Alps University), Wouter Joosen (imec-DistriNet, KU Leuven)

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ML-Leaks: Model and Data Independent Membership Inference Attacks and...

Ahmed Salem (CISPA Helmholtz Center for Information Security), Yang Zhang (CISPA Helmholtz Center for Information Security), Mathias Humbert (Swiss Data Science Center, ETH Zurich/EPFL), Pascal Berrang (CISPA Helmholtz Center for Information Security), Mario Fritz (CISPA Helmholtz Center for Information Security), Michael Backes (CISPA Helmholtz Center for Information Security)

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REDQUEEN: Fuzzing with Input-to-State Correspondence

Cornelius Aschermann (Ruhr-Universität Bochum), Sergej Schumilo (Ruhr-Universität Bochum), Tim Blazytko (Ruhr-Universität Bochum), Robert Gawlik (Ruhr-Universität Bochum), Thorsten Holz (Ruhr-Universität Bochum)

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Life after Speech Recognition: Fuzzing Semantic Misinterpretation for Voice...

Yangyong Zhang (Texas A&M University), Lei Xu (Texas A&M University), Abner Mendoza (Texas A&M University), Guangliang Yang (Texas A&M University), Phakpoom Chinprutthiwong (Texas A&M University), Guofei Gu (Texas A&M University)

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