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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Replication: Do We Snooze If We Can't Lose? Modelling...

Karoline Busse (University of Bonn); Dominik Wermke (Leibniz University Hannover); Sabrina Amft (University of Bonn); Sascha Fahl (Leibniz University Hannover); Emanuel von Zezschwitz, Matthew Smith (University of Bonn)

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Are some prices more equal than others? Evaluating store-based...

Hugo Jonker (Open University Netherlands), Stefan Karsch (TH Koln), Benjamin Krumnow (TH Koln), Godfried Meesters (Open University Netherlands)

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Adversarial Robustness for Tabular Data through Cost and Utility...

Klim Kireev (EPFL), Bogdan Kulynych (EPFL), Carmela Troncoso (EPFL)

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