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• W2980978419 abstract "Identifying features that leak information about sensitive attributes is a key challenge in the design of information obfuscation mechanisms. In this paper, we propose a framework to identify information-leaking features via information density estimation. Here, features whose information densities exceed a pre-defined threshold are deemed information-leaking features. Once these features are identified, we sequentially pass them through a targeted obfuscation mechanism with a provable leakage guarantee in terms of $mathsf{E}_gamma$-divergence. The core of this mechanism relies on a data-driven estimate of the trimmed information density for which we propose a novel estimator, named the trimmed information density estimator (TIDE). We then use TIDE to implement our mechanism on three real-world datasets. Our approach can be used as a data-driven pipeline for designing obfuscation mechanisms targeting specific features." @default.
• W2980978419 created "2019-10-25" @default.
• W2980978419 creator A5062359324 @default.
• W2980978419 creator A5074697940 @default.
• W2980978419 creator A5086525718 @default.
• W2980978419 date "2019-10-17" @default.
• W2980978419 modified "2023-09-23" @default.
• W2980978419 title "Obfuscation via Information Density Estimation." @default.
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• W2980978419 hasPublicationYear "2019" @default.
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