[Resource Topic] 2023/442: Non-interactive privacy-preserving naive Bayes classifier using homomorphic encryption

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Non-interactive privacy-preserving naive Bayes classifier using homomorphic encryption

Authors: Jingwei Chen, Yong Feng, Yang Liu, Wenyuan Wu, Guanci Yang


In this paper, we propose a non-interactive privacy-preserving naive Bayes classifier from leveled fully homomorphic encryption schemes. The classifier runs on a server that is also the model’s owner (modeler), whose input is the encrypted data from a client. The classifier produces encrypted classification results, which can only be decrypted by the client, while the modelers model is only accessible to the server. Therefore, the classifier does not leak any privacy on either the servers model or the clients data and results. More importantly, the classifier does not require any interactions between the server and the client during the classification phase. The main technical ingredient is an algorithm that computes the maximum index of an encrypted array homomorphically without any interactions. The proposed classifier is implemented using HElib. Experiments show the accuracy and efficiency of our classifier. For instance, the average cost can achieve about 34ms per sample for a real data set in UCI Machine Learning Repository with the security parameter about 100 and accuracy about 97%.

ePrint: https://eprint.iacr.org/2023/442

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