Kousha Kalantari

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We examine a tradeoff between privacy and utility in terms of local differential privacy (L-DP) and Hamming distortion for certain classes of finite-alphabet sources under Hamming distortion. We define two classes: permutation-invariant, and ordered statistics (whose probability mass functions are monotonic). We obtain the optimal L-DP mechanism for(More)
The tradeoff between privacy and utility is studied for small datasets using tools from fixed error asymptotics in information theory. The problem is formulated as determining the privacy mechanism (random mapping) which minimizes the mutual information (a metric for privacy leakage) between the private features of the original dataset and a released(More)
The privacy-utility tradeoff problem is formulated as determining the privacy mechanism (random mapping) that minimizes the mutual information (a metric for privacy leakage) between the private features of the original dataset and a released version. The minimization is subject to a constraint on the average distortion cost defined as a function f evaluated(More)
The privacy-utility tradeoff problem is formulated as determining the privacy mechanism (random mapping) that minimizes the mutual information (a metric for privacy leakage) between the private features of the original dataset and a released version. The minimization is studied with two types of constraints on the distortion between the public features and(More)
To be considered for the 2016 IEEE Jack Keil Wolf ISIT Student Paper Award. We develop the tradeoff between privacy, quantified using local differential privacy (L-DP), and utility, quantified using Hamming distortion, for specific classes of universal memoryless finite-alphabet sources. In particular, for the class of permutation invariant sources (i.e.,(More)
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