Separating Computational and Statistical Differential Privacy (Under Plausible Assumptions). (arXiv:2301.00104v1 [cs.CR])

Computational differential privacy (CDP) is a natural relaxation of the
standard notion of (statistical) differential privacy (SDP) proposed by Beimel,
Nissim, and Omri (CRYPTO 2008) and Mironov, Pandey, Reingold, and Vadhan
(CRYPTO 2009). In contrast to SDP, CDP only requires privacy guarantees to hold
against computationally-bounded adversaries rather than
computationally-unbounded statistical adversaries. Despite the question being
raised explicitly in several works (e.g., Bun, Chen, and Vadhan, TCC 2016), it
has remained tantalizingly open whether there is any task achievable with …

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