Parallel Linear Regression on Encrypted Data

In recent years, the advent of machine learning models on private data has been remarkable. However, in-corporating machine learning techniques to healthcare data is pretty challenging due to the privacy issues of sensitive data which restricts data sharing in plaintext. Ensuring the privacy of individuals in healthcare datasets while constructing a machine learning model is a challenging research problem today. This paper proposes an approximate mathematical model utilizing linear regression on homomorphically encrypted data to predict the disease association of an individual. Furthermore, as these encryption schemes are not efficient considering computation time, we incorporate the multi-core parallelism to make the framework realistic. We experimentally evaluate the performance of the proposed methods and report on the experimental results.