[Resource Topic] 2022/866: Communication Efficient Secure Logistic Regression

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Communication Efficient Secure Logistic Regression

Authors: Amit Agarwal, Stanislav Peceny, Mariana Raykova, Phillipp Schoppmann, and Karn Seth


We present a new two-party construction for secure logistic regression training, which enables two parties to train a logistic regression model on private secret shared data. Our goal is to minimize online communication and round complexity, while still allowing for an efficient offline phase. As part of our construction we develop many building blocks of independent interest. These include a new approximation technique for the sigmoid function, which results in a secure evaluation protocol with better communication; secure spline evaluation and secure powers computation protocols for fixed-point values; and a new comparison protocol that optimizes online communication. We also present a new two-party protocol for generating keys for distributed point functions (DPFs) over arithmetic sharing, where previous constructions do this only for Boolean outputs. We implement our protocol in an end-to-end system and benchmark its efficiency. We can securely evaluate a sigmoid in 20 ms online time and 1.12 KB of online communication. Our system can train a model over a database with 6000 samples and 5000 features with online communication of ~40 MB and online time of $9$min.

ePrint: https://eprint.iacr.org/2022/866

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