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• W2989964410 abstract "Differential privacy has become a widely accepted notion of privacy, leading to the introduction and deployment of numerous privatization mechanisms. However, ensuring the privacy guarantee is an error-prone process, both in designing mechanisms and in implementing those mechanisms. Both types of errors will be greatly reduced, if we have a data-driven approach to verify privacy guarantees, from a black-box access to a mechanism. We pose it as a property estimation problem, and study the fundamental trade-offs involved in the accuracy in estimated privacy guarantees and the number of samples required. We introduce a novel estimator that uses polynomial approximation of a carefully chosen degree to optimally trade-off bias and variance. With n samples, we show that this estimator achieves performance of a straightforward plug-in estimator with n*log(n) samples, a phenomenon referred to as effective sample size amplification. The minimax optimality of the proposed estimator is proved by comparing it to a matching fundamental lower bound." @default.
• W2989964410 created "2019-12-05" @default.
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• W2989964410 date "2019-01-01" @default.
• W2989964410 modified "2023-09-24" @default.
• W2989964410 title "Minimax Optimal Estimation of Approximate Differential Privacy on Neighboring Databases" @default.
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