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Creators/Authors contains: "Asoodeh, S"

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  1. ; ; ; ; ; (, PMLR)
    Accepted Manuscript Full Text Available
  2. ; ; ; ; ; ; (, Advances in neural information processing systems)
    Accepted Manuscript Full Text Available
  3. ; ; (, Proceedings of Machine Learning Research)
    null (Ed.)
    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 𝖤𝛾-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 \textit{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. 
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    Accepted Manuscript Full Text Available