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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YODA: Enabling computationally intensive contracts on blockchains with Byzantine...

Sourav Das (Department of Computer Science and Engineering, Indian Institute of Technology Delhi), Vinay Joseph Ribeiro (Department of Computer Science and Engineering, Indian Institute of Technology Delhi), Abhijeet Anand (Department of Computer Science and Engineering, Indian Institute of Technology Delhi)

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TIMBER-V: Tag-Isolated Memory Bringing Fine-grained Enclaves to RISC-V

Samuel Weiser (Graz University of Technology), Mario Werner (Graz University of Technology), Ferdinand Brasser (Technische Universität Darmstadt), Maja Malenko (Graz University of Technology), Stefan Mangard (Graz University of Technology), Ahmad-Reza Sadeghi (Technische Universität Darmstadt)

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A Treasury System for Cryptocurrencies: Enabling Better Collaborative Intelligence

Bingsheng Zhang (Lancaster University), Roman Oliynykov (IOHK Ltd.), Hamed Balogun (Lancaster University)

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NoDoze: Combatting Threat Alert Fatigue with Automated Provenance Triage

Wajih Ul Hassan (NEC Laboratories America, Inc.; University of Illinois at Urbana–Champaign), Shengjian Guo (Virginia Tech), Ding Li (NEC Laboratories America, Inc.), Zhengzhang Chen (NEC Laboratories America, Inc.), Kangkook Jee (NEC Laboratories America, Inc.), Zhichun Li (NEC Laboratories America, Inc.), Adam Bates (University of Illinois at Urbana–Champaign)

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Privacy Starts with UI: Privacy Patterns and Designer Perspectives in UI/UX Practice

Anxhela Maloku (Technical University of Munich), Alexandra Klymenko (Technical University of Munich), Stephen Meisenbacher (Technical University of Munich), Florian Matthes (Technical University of Munich)

Vision: Profiling Human Attackers: Personality and Behavioral Patterns in Deceptive Multi-Stage CTF Challenges

Khalid Alasiri (School of Computing and Augmented Intelligence Arizona State University), Rakibul Hasan (School of Computing and Augmented Intelligence Arizona State University)

From Underground to Mainstream Marketplaces: Measuring AI-Enabled NSFW Deepfakes on Fiverr

Mohamed Moustafa Dawoud (University of California, Santa Cruz), Alejandro Cuevas (Princeton University), Ram Sundara Raman (University of California, Santa Cruz)