Machine Learning with Partially Homomorphic Encrypted Data

Abstract

Machine learning had been widely used to analyze various kinds of data, including sensitive data such as medical and financial data. A trained machine learning model can be wrapped in a web application so that people can access it easily via internet. However, if the data to be analyzed is private or confidential, this will cause a problem; the application administrator may read the input. As shown by Dowlin et al. in their remarkable paper, this kind of problem can be solved with homomorphic encryption scheme. Paillier encryption scheme is one kind of encryption scheme that has homomorphic property. In this research, we will show that one type of machine learning model can take an input encrypted by Paillier encryption scheme and produce an encrypted output that shares the same key. A machine learning model will be trained with the MNIST database of hand-written digits. This model will be tested with the test data encrypted with Paillier encryption scheme. The experiment shows that the model achieved 92.92% accuracy on the test set.