Secure Logistic Regression Based on Homomorphic Encryption: Design and Evaluation.

ABSTRACT: BACKGROUND:Learning a model without accessing raw data has been an intriguing idea to security and machine learning researchers for years. In an ideal setting, we want to encrypt sensitive data to store them on a commercial cloud and run certain analyses without ever decrypting the data to preserve privacy. Homomorphic encryption technique is a promising candidate for secure data outsourcing, but it is a very challenging task to support real-world machine learning tasks. Existing frameworks can only handle simplified cases with low-degree polynomials such as linear means classifier and linear discriminative analysis. OBJECTIVE:The goal of this study is to provide a practical support to the mainstream learning models (eg, logistic regression). METHODS:We adapted a novel homomorphic encrypti

SUBMITTER: Kim M 

PROVIDER: S-EPMC5930176 | biostudies-literature | 2018 Apr

REPOSITORIES: biostudies-literature