PrivLogit: Efficient Privacy-preserving Logistic Regression by Tailoring Numerical Optimizers
By Wei Xie, Yang Wang, Steven M. Boker, Donald E. Brown. 2016
25 citations recorded by the source corpus.
Safeguarding privacy in machine learning is highly desirable, especially in collaborative studies across many organizations. Privacy-preserving distributed machine learning (based on cryptography) is popular to solve the problem. However, existing cryptographic protocols still incur excess computational overhead. Here, we make a novel observation that this is partially due to naive adoption of mainstream numerical optimization (e.g., Newton method) and failing to tailor for secure computing. This work presents a contrasting perspective: customizing numerical optimization specifically for secure settings. We propose a seemingly less-favorable optimization method that can in fact significantly accelerate privacy-preserving logistic regression. Leveraging this new method, we propose two new secure protocols for conducting logistic regression in a privacy-preserving and distributed manner. Extensive theoretical and empirical evaluations prove the competitive performance of our two secure proposals while without compromising accuracy or privacy: with speedup up to 2.3x and 8.1x, respectively, over state-of-the-art; and even faster as data scales up. Such drastic speedup is on top of and in addition to performance improvements from existing (and future) state-of-the-art cryptography. Our work provides a new way towards efficient and practical privacy-preserving logistic regression for large-scale studies which are common for modern science.
Subfields: Artificial Intelligence
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In Artificial Intelligence
- Miran Kim
- Yongsoo Song
- Andrey Kim
- Jung Hee Cheon
- Keewoo Lee
- Wei Dai
- Frederik Vercauteren
- Charlotte Bonte
Citations in the corpus
- Privacy-preserving logistic regression training (2018, background): “In [4] Xie et al. construct PrivLogit which performs logistic regression in a privacy-preserving but distributed manner.”
- Logistic regression model training based on the approximate homomorphic encryption (2018, methodology): “[12] used an additive HE scheme to aggregate some intermediate statistics.”