Differentially Private Bayesian Inference for Generalized Linear Models

Tejas Kulkarni, Joonas Jälkö, Antti Koskela, Samuel Kaski, Antti Honkela

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussaConference contributionScientificvertaisarvioitu

7 Lataukset (Pure)


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.

OtsikkoProceedings of the 38th International Conference on Machine Learning
ToimittajatM Meila, T Zhang
TilaJulkaistu - 2021
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisuussa
TapahtumaInternational Conference on Machine Learning - Virtual, Online
Kesto: 18 heinäkuuta 202124 heinäkuuta 2021
Konferenssinumero: 38


NimiProceedings of Machine Learning Research
ISSN (elektroninen)2640-3498


ConferenceInternational Conference on Machine Learning
KaupunkiVirtual, Online


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