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Differential privacy for symmetric log-concave mechanisms. (arXiv:2202.11393v2 [cs.CR] UPDATED)
July 5, 2022, 1:20 a.m. | Staal A. Vinterbo
cs.CR updates on arXiv.org arxiv.org
Adding random noise to database query results is an important tool for
achieving privacy. A challenge is to minimize this noise while still meeting
privacy requirements. Recently, a sufficient and necessary condition for
$(\epsilon, \delta)$-differential privacy for Gaussian noise was published.
This condition allows the computation of the minimum privacy-preserving scale
for this distribution. We extend this work and provide a sufficient and
necessary condition for $(\epsilon, \delta)$-differential privacy for all
symmetric and log-concave noise densities. Our results allow fine-grained …
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