On the relation between identifiability, differential privacy, and mutual-information privacy

By Weina Wang, Lei Ying, Junshan Zhang. 2014

179 citations recorded by the source corpus.

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Citations in the corpus

Deep Variational Information Bottleneck (2017, methodology): “…real images; using richer parametric marginal approximations, rather than assuming r ( z ) = N (0 , I ) ; exploring the connections to differential privacy (see e.g., Wang et al. (2016a); Cuff & Yu (2016)); and investigating open universe classiﬁcation problems (see e.g., Bendale & Boult (2015)).”
Extremal Mechanisms for Local Differential Privacy (2014, background): “Given Yi’s, the data analyst wants to test whether they are generated from M0 or M1.”
Differentially Private Testing of Identity and Closeness of Discrete Distributions (2017, background): “Information theoretic approaches to data privacy have also been studied in the past few years using quantities like mutual information, and guessing probability to quantify privacy [Mir12, SRP13, CY16, WYZ16, IW17].”
Differential Privacy as a Mutual Information Constraint (2016, background): “Another crucial ingredient that some of the literature fails to properly incorporate (e.g. [25]) when applying mutual information to diﬀerential privacy is that diﬀerential privacy is a property of the query mechanism and assumes no speciﬁc prior distribution on the database.”
Profile-based Privacy for Locally Private Computations (2019, background): “viewed in relation to information theoretic definitions dues to the deep connections present from differential privacy [7].”
Federated Learning with Bayesian Differential Privacy (2019, background): “computational DP [18], mutual-information privacy [19], [20], different versions of concentrated DP (CDP [21], zCDP [22], tCDP [23]), and Rényi DP (RDP) [24].”
Shredder: Learning Noise to Protect Privacy with Partial DNN Inference on the Edge (2019, background): “Mutual information has been widely used in the literature for both understanding the behaviour of neural networks [24, 25, 24, 26, 27, 28], and also to quantify information leakage in anonymity systems in the context of databases [29, 30, 31].”