Differential Privacy without Sensitivity

NeurIPS 2016 Kentaro MinamiHitomi AraiIssei SatoHiroshi Nakagawa

The exponential mechanism is a general method to construct a randomized estimator that satisfies $(\varepsilon, 0)$-differential privacy. Recently, Wang et al. showed that the Gibbs posterior, which is a data-dependent probability distribution that contains the Bayesian posterior, is essentially equivalent to the exponential mechanism under certain boundedness conditions on the loss function... (read more)

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