Kaustav Bhattacharjee, Aritra Dasgupta (New Jersey Institute of Technology)

The open data ecosystem is susceptible to vulnerabilities due to disclosure risks. Though the datasets are anonymized during release, the prevalence of the release-and-forget model makes the data defenders blind to privacy issues arising after the dataset release. One such issue can be the disclosure risks in the presence of newly released datasets which may compromise the privacy of the data subjects of the anonymous open datasets. In this paper, we first examine some of these pitfalls through the examples we observed during a red teaming exercise and then envision other possible vulnerabilities in this context. We also discuss proactive risk monitoring, including developing a collection of highly susceptible open datasets and a visual analytic workflow that empowers data defenders towards undertaking dynamic risk calibration strategies.

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Detection and Resolution of Control Decision Anomalies

Prof. Kang Shin (Kevin and Nancy O'Connor Professor of Computer Science, and the Founding Director of the Real-Time Computing Laboratory (RTCL) in the Electrical Engineering and Computer Science Department at the University of Michigan)

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Attacks as Defenses: Designing Robust Audio CAPTCHAs Using Attacks...

Hadi Abdullah (Visa Research), Aditya Karlekar (University of Florida), Saurabh Prasad (University of Florida), Muhammad Sajidur Rahman (University of Florida), Logan Blue (University of Florida), Luke A. Bauer (University of Florida), Vincent Bindschaedler (University of Florida), Patrick Traynor (University of Florida)

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Can You Tell Me the Time? Security Implications of...

Vik Vanderlinden, Wouter Joosen, Mathy Vanhoef (imec-DistriNet, KU Leuven)

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No Grammar, No Problem: Towards Fuzzing the Linux Kernel...

Alexander Bulekov (Boston University), Bandan Das (Red Hat), Stefan Hajnoczi (Red Hat), Manuel Egele (Boston University)

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