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However, it is important to consider users' privacy in the dataset, since results from data

Trusted execution environments (TEEs) canbeused to protect the content of the data during query computation, while supporting differential-private (DP) queries in TEEs provides record privacy when query output isrevealed.

Therefore, if ν() < +, then we can bound(10) with eαν(). To avoid crowded notations, we drop the conditioning onz from Pr[ |ρ = z]. The issue is how to proceed. Let φ be the standard normal density function andΦ be the CDF. The algorithm using SVT suchthat itonly releases the private answerstothe queries if the answer is sufficiently different from the "guess".

Wefirst establish minimax lower bounds for the estimation error, given avector of privacyguarantees, and show that a linear estimator is (near) optimal.

We consider how to privately share the personalized privacy losses incurred by objective perturbation, using per-instance differential privacy (pDP).