Privacy-preserving logistic regression training

By Charlotte Bonte, Frederik Vercauteren. 2018

109 citations recorded by the source corpus.

Logistic regression is a popular technique used in machine learning to construct classification models. Since the construction of such models is based on computing with large datasets, it is an appealing idea to outsource this computation to a cloud service. The privacy-sensitive nature of the input data requires appropriate privacy preserving measures before outsourcing it. Homomorphic encryption enables one to compute on encrypted data directly, without decryption and can be used to mitigate the privacy concerns raised by using a cloud service. In this paper, we propose an algorithm (and its implementation) to train a logistic regression model on a homomorphically encrypted dataset. The core of our algorithm consists of a new iterative method that can be seen as a simplified form of the fixed Hessian method, but with a much lower multiplicative complexity. We test the new method on two interesting real life applications: the first application is in medicine and constructs a model to predict the probability for a patient to have cancer, given genomic data as input; the second application is in finance and the model predicts the probability of a credit card transaction to be fraudulent. The method produces accurate results for both applications, comparable to running standard algorithms on plaintext data. This article introduces a new simple iterative algorithm to train a logistic regression model that is tailored to be applied on a homomorphically encrypted dataset. This algorithm can be used as a privacy-preserving technique to build a binary classification model and can be applied in a wide range of problems that can be modelled with logistic regression. Our implementation results show that our method can handle the large datasets used in logistic regression training.

Subfields: Artificial Intelligence

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In Artificial Intelligence

1. Shengping Zhou
2. Peng Liu
3. Di Niu
4. Jianming Yang
5. Linglong Kong
6. Yaochen Hu

Citations in the corpus

• Learning Privately over Distributed Features: An ADMM Sharing Approach (2019, background): “Another approach to privacy-preserving machine learning is through encryption Gilad-Bachrach et al. (2016); Takabi et al. (2016); Kikuchi et al. (2018) or secret sharing Mohassel and Zhang (2017); Wan et al. (2007); Bonte and Vercauteren (2018), so that models are trained on encrypted data.”
• FDML: A Collaborative Machine Learning Framework for Distributed Features (2019, background): “, via homomorphic encryption [11, 28] or secret sharing [3, 22, 29].”