Differentially Private Bayesian Inference for Generalized Linear Models

Generalized linear models (GLMs) such as logistic regression are among the most widely used arms in data analyst's repertoire and often used on sensitive datasets. A large body of prior works that investigate GLMs under differential privacy (DP) constraints provide only private point estimates of the regression coefficients, and are not able to quantify parameter uncertainty. In this work, with logistic and Poisson regression as running examples, we introduce a generic noise-aware DP Bayesian inference method for a GLM at hand, given a noisy sum of summary statistics. Quantifying uncertainty allows us to determine which of the regression coefficients are statistically significantly different from zero. We provide a tight privacy analysis and experimentally demonstrate that the posteriors obtained from our model, while adhering to strong privacy guarantees, are close to the non-private posteriors.

Citation

Kulkarni, T, Jälkö, J, Koskela, A, Kaski, S & Honkela, A 2021, Differentially Private Bayesian Inference for Generalized Linear Models . in M Meila & T Zhang (eds), Proceedings of the 38th International Conference on Machine Learning . Proceedings of Machine Learning Research, vol. 139, JMLR, pp. 5838-5849, International Conference on Machine Learning, Virtual, Online, 18/07/2021 . < http://proceedings.mlr.press/v139/kulkarni21a.html >

Permanent link to this item

https://urn.fi/URN:NBN:fi:aalto-2021112410369

Collections

[article-cris] Perustieteiden korkeakoulu / SCI

Show all metadata

Differentially Private Bayesian Inference for Generalized Linear Models

Access rights

URL

Journal Title

Journal ISSN

Volume Title

Authors

Date

Department

Major/Subject

Mcode

Degree programme

Language

Pages

Series

Abstract

Description

Keywords

Other note

Citation

Permanent link to this item

Collections