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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Filipo Sharevski (DePaul University), Amy Devine (DePaul University), Emma Pieroni (DePaul University), Peter Jachim (DePaul University)

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Him of Many Faces: Characterizing Billion-scale Adversarial and Benign...

Shujiang Wu (Johns Hopkins University), Pengfei Sun (F5, Inc.), Yao Zhao (F5, Inc.), Yinzhi Cao (Johns Hopkins University)

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The “Beatrix” Resurrections: Robust Backdoor Detection via Gram Matrices

Wanlun Ma (Swinburne University of Technology), Derui Wang (CSIRO’s Data61), Ruoxi Sun (The University of Adelaide & CSIRO's Data61), Minhui Xue (CSIRO's Data61), Sheng Wen (Swinburne University of Technology), Yang Xiang (Digital Research & Innovation Capability Platform, Swinburne University of Technology)

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Short: Rethinking Secure Pairing in Drone Swarms

Muslum Ozgur Ozmen, Habiba Farrukh, Hyungsub Kim, Antonio Bianchi, Z. Berkay Celik (Purdue University)

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