New Privacy Mechanism Design With Direct Access to the Private Data
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The design of a statistical signal processing privacy problem is studied where the private data is assumed to be observable. In this work, an agent observes useful data Y, which is correlated with private data X, and wants to disclose the useful information to a user. A statistical privacy mechanism is employed to generate data U based on (X,Y) that maximizes the revealed information about Y while satisfying a privacy criterion. To this end, we use extended versions of the Functional Representation Lemma and Strong Functional Representation Lemma and combine them with a simple observation which we call separation technique. New lower bounds on privacy-utility trade-off are derived and we show that they can improve the previous bounds. We study the obtained bounds in different scenarios and compare them with previous results.
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