Logistic regression model training based on the approximate homomorphic encryption.

ABSTRACT:

Background

Security concerns have been raised since big data became a prominent tool in data analysis. For instance, many machine learning algorithms aim to generate prediction models using training data which contain sensitive information about individuals. Cryptography community is considering secure computation as a solution for privacy protection. In particular, practical requirements have triggered research on the efficiency of cryptographic primitives.

Methods

This paper presents a method to train a logistic regression model without information leakage. We apply the homomorphic encryption scheme of Cheon et al. (ASIACRYPT 2017) for an efficient arithmetic over real numbers, and devise a new encoding method to reduce storage of encrypted database. In addition, we adapt

SUBMITTER: Kim A 

PROVIDER: S-EPMC6180367 | biostudies-literature | 2018 Oct

REPOSITORIES: biostudies-literature