Privacy-preserving parametric inference: a case for robust statistics

Marco Avella-Medina

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Abstract:Differential privacy is a cryptographically-motivated approach to privacy that has become a very active field of research over the last decade in theoretical computer science and machine learning. In this paradigm one assumes there is a trusted curator who holds the data of individuals in a database and the goal of privacy is to simultaneously protect individual data while allowing the release of global characteristics of the database. In this setting we introduce a general framework for parametric infe ...
Can a differentially private estimator be characterized via its influence function?
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23 months ago
Emily ▴ 25

Medina shows that bounded influence estimators can be naturally used to construct differentially private estimators. However, he also shows that there are differentially private estimators which do not have an asymptotically bounded influence function.

Is there a condition on the influence function of an estimator such that medina's associated estimator or some other natural associated estimator will be differentially private if and only if it satisfies this condition?

Statistics Robust Differential Privacy • 500 views

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