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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Jacob Hopkins (Texas A&M University - Corpus Christi), Carlos Rubio-Medrano (Texas A&M University - Corpus Christi), Cori Faklaris (University of North Carolina at Charlotte)

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Zaina Aljallad (University of Central Florida); Wentao Guo (Pomona College); Chhaya Chouhan, Christy Laperriere (University of Central Florida); Jess Kropczynski (University of Cincinnati); Pamela Wisnewski (University of Central Florida); Heather Lipford (University of North Carolina at Charlotte)

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Alexandra Klymenko (Technical University of Munich), Stephen Meisenbacher (Technical University of Munich), Luca Favaro (Technical University of Munich), and Florian Matthes (Technical University of Munich)

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