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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Noah T. Curran (University of Michigan), Kang G. Shin (University of Michigan), William Hass (Lear Corporation), Lars Wolleschensky (Lear Corporation), Rekha Singoria (Lear Corporation), Isaac Snellgrove (Lear Corporation), Ran Tao (Lear Corporation)

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Double and Nothing: Understanding and Detecting Cryptocurrency Giveaway Scams

Xigao Li (Stony Brook University), Anurag Yepuri (Stony Brook University), Nick Nikiforakis (Stony Brook University)

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Jaewan Seo, Jiwon Kwak, Seungjoo Kim (Korea University)

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Jiayun Fu (Huazhong University of Science and Technology), Xiaojing Ma (Huazhong University of Science and Technology), Bin B. Zhu (Microsoft Research Asia), Pingyi Hu (Huazhong University of Science and Technology), Ruixin Zhao (Huazhong University of Science and Technology), Yaru Jia (Huazhong University of Science and Technology), Peng Xu (Huazhong University of Science and Technology), Hai…

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