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Differential privacy for metric spaces : information-theoretic models for privacy and utility with new applications to metric domains

Abstract : Differential privacy, introduced by Dwork et al. in 2006, has become the benchmark for data privacy in statistical datasets. Despite its widespread popularity, its use in other domains has been relatively limited. In this thesis, we explore a generalisation of differential privacy for metric domains known as d-privacy. Our approach incorporates an information-theoretic framework for analysing information flows, which allows us to provide a structural characterisation of d-privacy and analyse its privacy and utility properties. Using information flow analysis we examine the leakage order of channels induced by the privacy parameter epsilon, we find a new characterisation for optimal mechanisms, extending existing results in the area of universal optimality, and we re-examine the privacy-utility trade-off for oblivious and local differential privacy workflows. Finally, we demonstrate the applicability of d-privacy to novel and complex domains with example applications in text document privacy, statistical utility and private nearest neighbour search.
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Submitted on : Wednesday, September 15, 2021 - 9:34:12 AM
Last modification on : Thursday, September 16, 2021 - 3:40:14 AM

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Natasha Fernandes. Differential privacy for metric spaces : information-theoretic models for privacy and utility with new applications to metric domains. Information Theory [cs.IT]. Institut Polytechnique de Paris; Macquarie university (Sydney, Australie), 2021. English. ⟨NNT : 2021IPPAX030⟩. ⟨tel-03344453⟩

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