Cryptology ePrint Archive: Report 2021/208

Secure Poisson Regression

Mahimna Kelkar and Phi Hung Le and Mariana Raykova and Karn Seth

Abstract: We introduce the first construction for secure two-party computation of Poisson regression, which enables two parties who hold shares of the input samples to learn only the resulting Poisson model while protecting the privacy of the inputs.

Our construction relies on new protocols for secure fixed-point exponentiation and correlated matrix multiplications. Our secure exponentiation construction avoids expensive bit decomposition and achieves orders of magnitude improvement in both online and offline costs over state of the art works. As a result, the dominant cost for our secure Poisson regression are matrix multiplications with one fixed matrix. We introduce a new technique, called correlated Beaver triples, which enables many such multiplications at the cost of roughly one matrix multiplication. This further brings down the cost of secure Poisson regression.

We implement our constructions and show their extreme efficiency. In a LAN setting, our secure exponentiation for 20-bit fractional precision takes less than 0.07ms with a batch-size of 100,000. One iteration of secure Poisson regression on a dataset with 10,000 samples with 1000 binary features needs about 65.82s in the offline phase, 55.14s in the online phase and 17MB total communication. For several real datasets this translates into training that takes seconds and only a couple of MB communication.

Category / Keywords: cryptographic protocols / Poisson regression, Fixed-point exponentiation

Original Publication (with major differences): USENIX Security 2022

Date: received 25 Feb 2021, last revised 13 Oct 2021

Contact author: mahimna at cs cornell edu

Available format(s): PDF | BibTeX Citation

Version: 20211013:193440 (All versions of this report)

Short URL: ia.cr/2021/208


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