Efficient, Differentially Private Point Estimators

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dc.creatorSmith, Adam
dc.date2008-09-27
dc.date.accessioned2026-07-07T10:06:01Z
dc.date.available2026-07-07T10:06:01Z
dc.descriptionDifferential 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.
dc.description9 pages
dc.identifierhttps://arxiv.org/abs/0809.4794
dc.identifierhttp://arxiv.org/abs/0809.4794
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/170189
dc.subjectCryptography and Security
dc.subjectData Structures and Algorithms
dc.titleEfficient, Differentially Private Point Estimators
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