Secure and Efficient Regression Analysis Using a Hybrid Cryptographic Framework: Development and Evaluation.

ABSTRACT: BACKGROUND:Machine learning is an effective data-driven tool that is being widely used to extract valuable patterns and insights from data. Specifically, predictive machine learning models are very important in health care for clinical data analysis. The machine learning algorithms that generate predictive models often require pooling data from different sources to discover statistical patterns or correlations among different attributes of the input data. The primary challenge is to fulfill one major objective: preserving the privacy of individuals while discovering knowledge from data. OBJECTIVE:Our objective was to develop a hybrid cryptographic framework for performing regression analysis over distributed data in a secure and efficient way. METHODS:Existing secure computation schemes ar

SUBMITTER: Sadat MN 

PROVIDER: S-EPMC5859787 | biostudies-literature | 2018 Mar

REPOSITORIES: biostudies-literature