The goal of this study is to provide a practical support to the mainstream learning models (eg logistic regression). We adapted a novel homomorphic encryption scheme optimized for real numbers computation. We devised (1) the least squares approximation of the logistic function for accuracy and efficiency (ie reduce computation cost) and (2) new packing and parallelization techniques.
Using real-world datasets, we evaluated the performance of our model and demonstrated its feasibility in speed and memory consumption. For example, it took about 116 minutes to obtain the training model from homomorphically encrypted Edinburgh dataset. In addition, it gives fairly accurate predictions on the testing dataset. We present the first homomorphically encrypted logistic regression outsourcing model based on the critical observation that the precision loss of classification models is sufficiently small so that the decision plan stays still.
Category / Keywords: Homomorphic encryption, approximate arithmetic, logistic regression, gradient descent Original Publication (with minor differences): JMIR Medical Informatics