Privacy-preserving analytics : secure logistic regression
Date of Issue2019
School of Computer Science and Engineering
Much data and information have been collected about us from all aspects of our life. Sometimes, we need to do analysis on this data without violating the privacy of individuals. In this project, we present a cryptographic library that can be used to do logistic regression under encrypted data. The encryption scheme used is a multiparty computation based on Exponential ElGamal. A special type of multiplication gate, the conditional gate, helps in the realization of the library. An implementation of the library usage on predicting the severity of heart disease based on the encrypted patient’s attributes is also presented along this project.
DRNTU::Engineering::Computer science and engineering
Final Year Project (FYP)
Nanyang Technological University