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Randomized Response with Gradual Release of Privacy Budget
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An algorithm is developed to gradually relax the Differential Privacy (DP) guarantee of a randomized response. The output from each relaxation maintains the same probability distribution as a standard randomized response with the equivalent DP guarantee, ensuring identical utility as the standard approach. The entire relaxation process is proven to have the same DP guarantee as the most recent relaxed guarantee. The DP relaxation algorithm is adaptable to any Local Differential Privacy (LDP) mechanisms relying on randomized response. It has been seamlessly integrated into RAPPOR, an LDP crowdsourcing string-collecting tool, to optimize the utility of estimating the frequency of collected data. Additionally, it facilitates the relaxation of the DP guarantee for mean estimation based on randomized response. Finally, numerical experiments have been conducted to validate the utility and DP guarantee of the algorithm.
Forward citations
Cited by 1 Pith paper
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Private Lossless Multiple Release
Lossless multiple release, where any set of private releases is only as revealing as the least private one, is achievable for a broad class of additive noise mechanisms including Gaussian, Laplace, and Poisson.
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