Differentially Private Bayesian Inference for Exponential Families

被引：0

作者：

Bernstein, Garrett ^{[1
]}

Sheldon, Daniel ^{[1
]}

机构：

[1] Univ Massachusetts Amherst, Coll Informat & Comp Sci, Amherst, MA 01002 USA

来源：

ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 31 (NIPS 2018) | 2018年 / 31卷

基金：

美国国家科学基金会;

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The study of private inference has been sparked by growing concern regarding the analysis of data when it stems from sensitive sources. We present the first method for private Bayesian inference in exponential families that properly accounts for noise introduced by the privacy mechanism. It is efficient because it works only with sufficient statistics and not individual data. Unlike other methods, it gives properly calibrated posterior beliefs in the non-asymptotic data regime.

引用

页数：11

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