Privacy-preserving Naive Bayes classification based on secure two-party computation - Liu, Kun; Tang, Chunming (AIMS mathematics, 2023)
With the proliferation of data and machine learning techniques, there is a growing need to develop methods that enable collaborative training and prediction of sensitive data while preserving privacy. This paper proposes a new protocol for privacy-preserving Naive Bayes classification using secure two-party computation (STPC). The key idea is to split the training data between two non-colluding servers using STPC to train the model without leaking information. The servers secretly share their data and the intermediate computations using cryptographic techniques like Beaver's multiplication triples and Yao's garbled circuits. We implement and evaluate our protocols on the MNIST dataset, demonstrating that they achieve the same accuracy as plaintext computation with reasonable overhead. A formal security analysis in the semi-honest model shows that the scheme protects the privacy of the training data. Our work advances privacy-preserving machine learning by enabling secure outsourced Naive Bayes classification with applications such as fraud detection, medical diagnosis, and predictive analytics on confidential data from multiple entities. The modular design allows embedding different secure matrix multiplication techniques, making the framework adaptable. This line of research paves the way for practical and secure data mining in a distributed manner, upholding stringent privacy regulations.