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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RR: A Fault Model for Efficient TEE Replication

Baltasar Dinis (Instituto Superior Técnico (IST-ULisboa) / INESC-ID / MPI-SWS), Peter Druschel (MPI-SWS), Rodrigo Rodrigues (Instituto Superior Técnico (IST-ULisboa) / INESC-ID)

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Unlocking the Potential of Domain Aware Binary Analysis in...

Dr. Zhiqiang Lin (Distinguished Professor of Engineering at The Ohio State University)

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“I didn't click”: What users say when reporting phishing

Nikolas Pilavakis, Adam Jenkins, Nadin Kokciyan, Kami Vaniea (University of Edinburgh)

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Breaking and Fixing Virtual Channels: Domino Attack and Donner

Lukas Aumayr (TU Wien), Pedro Moreno-Sanchez (IMDEA Software Institute), Aniket Kate (Purdue University / Supra), Matteo Maffei (Christian Doppler Laboratory Blockchain Technologies for the Internet of Things / TU Wien)

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