Efficient, Differentially Private Point Estimators

Details Efficient, Differentially Private Point Estimators Efficient, Differentially Private Point Estimators

2008-09-27

arxiv.org/abs/0809.4794v1

Computer Science

Cryptography and Security

9 pages

Scientific paper

Differential privacy is a recent notion of privacy for statistical databases that provides rigorous, meaningful confidentiality guarantees, even in the presence of an attacker with access to arbitrary side information. We show that for a large class of parametric probability models, one can construct a differentially private estimator whose distribution converges to that of the maximum likelihood estimator. In particular, it is efficient and asymptotically unbiased. This result provides (further) compelling evidence that rigorous notions of privacy in statistical databases can be consistent with statistically valid inference.

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