Secure KNN Classification Scheme Based on Homomorphic Encryption for Cyberspace

Author:

Liu Jiasen12ORCID,Wang Chao2ORCID,Tu Zheng2,Wang Xu An12ORCID,Lin Chuan1ORCID,Li Zhihu3

Affiliation:

1. Guizhou Provincial Key Laboratory of Public Big Data, GuiZhou University, Guiyang 550025, China

2. Key Laboratory for Network and Information Security of the PAP, Engineering University of the PAP, Xi’an 710086, China

3. China Electric Power Research Institute, Beijing 100001, China

Abstract

With the advent of the intelligent era, more and more artificial intelligence algorithms are widely used and a large number of user data are collected in the cloud server for sharing and analysis, but the security risks of private data breaches are also increasing in the meantime. CKKS homomorphic encryption has become a research focal point in the cryptography field because of its ability of homomorphic encryption for floating-point numbers and comparable computational efficiency. Based on the CKKS homomorphic encryption, this paper implements a secure KNN classification scheme in cloud servers for Cyberspace (CKKSKNNC) and supports batch calculation. This paper uses the CKKS homomorphic encryption scheme to encrypt user data samples and then uses Euclidean distance, Pearson similarity, and cosine similarity to compute the similarity between ciphertext data samples. Finally, the security classification of the samples is realized by voting rules. This paper selects IRIS data set for experimental, which is the classification data set commonly used in machine learning. The experimental results show that the accuracy of the other three similarity algorithms of the IRIS data is around 97% except for the Pearson correlation coefficient, which is almost the same as that in plaintext, which proves the effectiveness of this scheme. Through comparative experiments, the efficiency of this scheme is proved.

Funder

Foundation of State Key Laboratory of Public Big Data

Publisher

Hindawi Limited

Subject

Computer Networks and Communications,Information Systems

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