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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Sarisht Wadhwa (Duke University), Jannis Stoeter (Duke University), Fan Zhang (Duke University, Yale University), Kartik Nayak (Duke University)

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Wenjie Qu (Huazhong University of Science and Technology), Jinyuan Jia (University of Illinois Urbana-Champaign), Neil Zhenqiang Gong (Duke University)

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Your Router is My Prober: Measuring IPv6 Networks via...

Long Pan (Tsinghua University), Jiahai Yang (Tsinghua University), Lin He (Tsinghua University), Zhiliang Wang (Tsinghua University), Leyao Nie (Tsinghua University), Guanglei Song (Tsinghua University), Yaozhong Liu (Tsinghua University)

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UsersFirst in Practice: Evaluating a User-Centric Threat Modeling Taxonomy...

Alexandra Xinran Li (Carnegie Mellon University), Tian Wang (University of Illinois Urbana-Champaign), Yu-Ju Yang (University of Illinois Urbana-Champaign), Miguel Rivera-Lanas (Carnegie Mellon University), Debeshi Ghosh (Carnegie Mellon University), Hana Habib (Carnegie Mellon University), Lorrie Cranor (Carnegie Mellon University), Norman Sadeh (Carnegie Mellon University)

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