PrivESD: A Privacy-Preserving Cloud-Edge Collaborative Logistic Regression Model Over Encrypted Streaming Data
Chen Wang, Jian Feng Xu, Jing Chen, Vijay Varadharajan, Cody Lewis · IEEE Transactions on Dependable and Secure Computing · 2025
Outsourcing logistic regression classification services to the cloud is highly beneficial for streaming data. However, it raises critical privacy concerns for the input data and the training models. Current solutions for encrypted logistic regression classification fall short in the processing of encrypted streaming data. In this paper, we propose a privacy-preserving logistic regression model (PrivESD), which allows computation over encrypted streaming data. First, we propose a lightweight framework, which creates a collaborative workload between the cloud and the edge thereby reducing the computation complexity of the cloud and the number of communications between the data owners and the cloud. Second, we develop a tailored building block library with strong data confidentiality that supports comparison operations. This library has then been used to construct logistic regression. Finally, we devise a processing scheme that uses stochastic gradient descent with momentum to train the model to prevent the problem of local optimal convergence with streaming data. We have conducted extensive performance analysis demonstrating that the proposed protocols such as our secure compare protocol outperforms existing schemes such as Bost [44] and Guo [45], and that the encryption and decryption operations of PrivESD are similar to that of Paillier [42] with$2^{30}$keys.