Logistic regression model training based on the approximate homomorphic encryption

• Published in: BMC medical genomics Vol. 11; no. Suppl 4; p. 83
• Main Authors: Kim, Andrey, Song, Yongsoo, Kim, Miran, Lee, Keewoo, Cheon, Jung Hee
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 11-10-2018; BioMed Central; BMC
• Subjects: Artificial intelligence; Computer applications; Confidentiality; Cryptography; Data analysis; Data processing; Data security; Datasets; Homomorphic encryption; Informatics; Innovations; International conferences; Learning algorithms; Logistic regression; Machine learning; Management; Mathematics; Medical records; Methods; Performance evaluation; Privacy; Regression analysis; Safety and security measures; Logistic regression; Homomorphic encryption; Machine learning
• ISSN: 1755-8794; 1755-8794
• DOI: 10.1186/s12920-018-0401-7

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. 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 Nesterov's accelerated gradient method to reduce the number of iterations as well as the computational cost while maintaining the quality of an output classifier. Our method shows a state-of-the-art performance of homomorphic encryption system in a real-world application. The submission based on this work was selected as the best solution of Track 3 at iDASH privacy and security competition 2017. For example, it took about six minutes to obtain a logistic regression model given the dataset consisting of 1579 samples, each of which has 18 features with a binary outcome variable. We present a practical solution for outsourcing analysis tools such as logistic regression analysis while preserving the data confidentiality.