PrivESD: A Privacy-Preserving Cloud-Edge Collaborative Logistic Regression Model Over Encrypted Streaming Data

Summary

PrivESD is a privacy-preserving logistic regression model that performs computation over encrypted streaming data, splitting work between a cloud and an edge device. The authors build a building-block library supporting secure comparison, use it to construct logistic regression, and train with stochastic gradient descent with momentum. They report that their secure compare protocol outperforms the schemes of Bost [44] and Guo [45], and that its encryption and decryption costs are similar to Paillier [42] with 2^30 keys.