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Differentially Private Bayesian Inference for Generalized Linear Models
Tejas Kulkarni · Joonas Jälkö · Antti Koskela · Samuel Kaski · Antti Honkela

Thu Jul 22 06:30 PM -- 06:35 PM (PDT) @
in Security »

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 previously unknown 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.

Keywords: Privacy, Anonymity, and Security · Social Aspects of Machine Learning

Author Information

Tejas Kulkarni (Aalto University)
Joonas Jälkö (Aalto University)
Antti Koskela (University of Helsinki)
Samuel Kaski (Aalto University and University of Manchester)
Antti Honkela (University of Helsinki)

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