Secure kNN for Distributed Cloud Environment Using Fully Homomorphic Encryption

doi:10.1109/TCC.2025.3561586

Secure kNN for Distributed Cloud Environment Using Fully Homomorphic Encryption

2025-04-01

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DOI：10.1109/TCC.2025.3561586

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Privacy-preserving k-nearest neighbor (PPkNN) classification for multiple clouds enables categorizing queried data into a class in keeping with data privacy, where the database and key servers jointly perform cryptographic operations. The existing solutions, unfortunately, take a long time and incur a large amount of traffic between the database and key servers. Therefore, in this article, we propose a fast and secure kNN classification protocol, namely FSkNN, over distributed databases deployed in multiple clouds under the semi-honest model. Particularly, we focus on optimizing the network-related operations during kNN classification. That is, the proposed cryptographic protocol reduces the number of interactions between the servers by using a fully homomorphic encryption scheme and eliminates unnecessary traffic by applying mathematical techniques. In addition, the indistinguishability-based security of FSkNN is proven. We implemented FSkNN with C++ and the testbed experiments demonstrate that the proposed scheme significantly facilitates the query response time and reduces the communication cost.

Keywords:

Privacy-preserving k-nearest neighbor

PPkNN

security and privacy

Secure kNN for Distributed Cloud Environment Using Fully Homomorphic Encryption

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