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

Chen Wang, Jian Xu, Jiarun Li, Yan Dong, Nitin Naik

Research output: Contribution to journalArticlepeer-review


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.
Original languageEnglish
Pages (from-to)1421-1436
Number of pages16
JournalIntelligent Automation & Soft Computing
Issue number2
Publication statusPublished - 21 Jun 2023

Bibliographical note

Copyright 2023. The authors. 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.

Funding: This work was supported in part by the National Natural Science Foundation of China under Grant No. 61872069, in part by the Fundamental Research Funds for the Central Universities under Grant N2017012


  • Outsourced privacy-preserving
  • kNN
  • machine learning
  • multi-key HE


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