A realisation-level privacy filter for adaptive differential privacy queries that guarantees (ε, δ)-DP and improves utility over standard worst-case composition methods.
Our data, ourselves: Privacy via distributed noise generation
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Chernoff DP is sandwiched between KL DP and ε-DP, outperforms KL in numerical Laplace-mechanism tests, and yields a new upper bound on adversary membership advantage compared with (ε,δ)-DP bounds.
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Realisation-Level Privacy Filtering
A realisation-level privacy filter for adaptive differential privacy queries that guarantees (ε, δ)-DP and improves utility over standard worst-case composition methods.
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Chernoff Information as a Privacy Constraint for Adversarial Classification and Membership Advantage
Chernoff DP is sandwiched between KL DP and ε-DP, outperforms KL in numerical Laplace-mechanism tests, and yields a new upper bound on adversary membership advantage compared with (ε,δ)-DP bounds.