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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Yanzuo Chen (The Hong Kong University of Science and Technology), Yuanyuan Yuan (The Hong Kong University of Science and Technology), Shuai Wang (The Hong Kong University of Science and Technology)

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Modeling End-User Affective Discomfort With Mobile App Permissions Across...

Yuxi Wu (Georgia Institute of Technology and Northeastern University), Jacob Logas (Georgia Institute of Technology), Devansh Ponda (Georgia Institute of Technology), Julia Haines (Google), Jiaming Li (Google), Jeffrey Nichols (Apple), W. Keith Edwards (Georgia Institute of Technology), Sauvik Das (Carnegie Mellon University)

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Cross-National Study on Phishing Resilience

Shakthidhar Reddy Gopavaram (Indiana University), Jayati Dev (Indiana University), Marthie Grobler (CSIRO’s Data61), DongInn Kim (Indiana University), Sanchari Das (University of Denver), L. Jean Camp (Indiana University)

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Efficient Privacy-Preserved Processing of Multimodal Data for Vehicular Traffic...

Meisam Mohammady (Iowa State University), Reza Arablouei (Data61, CSIRO)

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