Differentially private distributed Bayesian linear regression with MCMC

Alparslan, Barış and Yıldırım, Sinan and Birbil, Ş. İlker (2023) Differentially private distributed Bayesian linear regression with MCMC. In: 40th International Conference on Machine Learning, Honolulu, Hawaii, USA

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We propose a novel Bayesian inference framework for distributed differentially private linear regression. We consider a distributed setting where multiple parties hold parts of the data and share certain summary statistics of their portions in privacy-preserving noise. We develop a novel generative statistical model for privately shared statistics, which exploits a useful distributional relation between the summary statistics of linear regression. We propose Bayesian estimation of the regression coefficients, mainly using Markov chain Monte Carlo algorithms, while we also provide a fast version that performs approximate Bayesian estimation in one iteration. The proposed methods have computational advantages over their competitors. We provide numerical results on both real and simulated data, which demonstrate that the proposed algorithms provide well-rounded estimation and prediction.
Item Type: Papers in Conference Proceedings
Uncontrolled Keywords: Differential Privacy, Linear Regression, Markov chain Monte Carlo
Subjects: Q Science > QA Mathematics > QA273-280 Probabilities. Mathematical statistics
Divisions: Faculty of Engineering and Natural Sciences > Academic programs > Computer Science & Eng.
Faculty of Engineering and Natural Sciences
Depositing User: Sinan Yıldırım
Date Deposited: 04 Oct 2023 14:54
Last Modified: 07 Feb 2024 14:56
URI: https://research.sabanciuniv.edu/id/eprint/48181

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