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Outsourced Privacy-Preserving kNN Classifier Model Based on Multi-Key Homomorphic Encryption

Chen Wang1, Jian Xu1,*, Jiarun Li1, Yan Dong1, Nitin Naik2

1 Software College, Northeastern University, Shenyang, 110169, China
2 School of Informatics and Digital Engineering, Aston University, Birmingham, B15 2TT, UK

* Corresponding Author: Jian Xu. Email: email

Intelligent Automation & Soft Computing 2023, 37(2), 1421-1436. https://doi.org/10.32604/iasc.2023.034123

Abstract

Outsourcing the k-Nearest Neighbor (kNN) classifier to the cloud is useful, yet it will lead to serious privacy leakage due to sensitive outsourced data and models. In this paper, we design, implement and evaluate a new system employing an outsourced privacy-preserving kNN Classifier Model based on Multi-Key Homomorphic Encryption (kNNCM-MKHE). We firstly propose a security protocol based on Multi-key Brakerski-Gentry-Vaikuntanathan (BGV) for collaborative evaluation of the kNN classifier provided by multiple model owners. Analyze the operations of kNN and extract basic operations, such as addition, multiplication, and comparison. It supports the computation of encrypted data with different public keys. At the same time, we further design a new scheme that outsources evaluation works to a third-party evaluator who should not have access to the models and data. In the evaluation process, each model owner encrypts the model and uploads the encrypted models to the evaluator. After receiving encrypted the kNN classifier and the user’s inputs, the evaluator calculated the aggregated results. The evaluator will perform a secure computing protocol to aggregate the number of each class label. Then, it sends the class labels with their associated counts to the user. Each model owner and user encrypt the result together. No information will be disclosed to the evaluator. The experimental results show that our new system can securely allow multiple model owners to delegate the evaluation of kNN classifier.

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APA Style
Wang, C., Xu, J., Li, J., Dong, Y., Naik, N. (2023). Outsourced privacy-preserving knn classifier model based on multi-key homomorphic encryption. Intelligent Automation & Soft Computing, 37(2), 1421-1436. https://doi.org/10.32604/iasc.2023.034123
Vancouver Style
Wang C, Xu J, Li J, Dong Y, Naik N. Outsourced privacy-preserving knn classifier model based on multi-key homomorphic encryption. Intell Automat Soft Comput . 2023;37(2):1421-1436 https://doi.org/10.32604/iasc.2023.034123
IEEE Style
C. Wang, J. Xu, J. Li, Y. Dong, and N. Naik "Outsourced Privacy-Preserving kNN Classifier Model Based on Multi-Key Homomorphic Encryption," Intell. Automat. Soft Comput. , vol. 37, no. 2, pp. 1421-1436. 2023. https://doi.org/10.32604/iasc.2023.034123



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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