Secure KNN Classification Scheme Based on Homomorphic Encryption for Cyberspace

Jiasen Liu,Xu An Wang,Zheng Tu,Zhihu Li,Chuan Lin,Chao Wang,Gu Zhaoquan

doi:10.1155/2021/8759922

Jiasen Liu, Xu An Wang + Show 5 more

Open Access

https://doi.org/10.1155/2021/8759922

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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.

Highlights

With the gradual maturity of various AI algorithms, data has gradually become the basis of social operation, playing an essential role in important areas such as economic investment, social management, scientific and technological development, and national security
In 2014, Elmehdwi et al [25] proposed a secure KNN scheme in an outsourcing environment based on the Paillier homomorphic encryption scheme, which can query the data without leaking any information to the cloud server by using the feature of homomorphic encryption and hides the query and data access mode of users, but the computing cost is large
Since the cloud service provider (CSP) is “honest and curious”, the transmission network may be subject to malicious attacks. erefore, we list the following security issues that may occur when users upload data to the cloud server for KNN classification: (1) CSP may strictly abide by the designed protocol, but it can infer other additional information through the information legally received in the process of the protocol

Summary

Introduction

With the gradual maturity of various AI algorithms, data has gradually become the basis of social operation, playing an essential role in important areas such as economic investment, social management, scientific and technological development, and national security. In 2014, Elmehdwi et al [25] proposed a secure KNN scheme in an outsourcing environment based on the Paillier homomorphic encryption scheme, which can query the data without leaking any information to the cloud server by using the feature of homomorphic encryption and hides the query and data access mode of users, but the computing cost is large. In 2016 and 2017, based on the Paillier homomorphic encryption scheme, Kim et al [28, 29] designed a privacy protection KNN classification algorithm using the tree index structure and Yao’s garbled code, respectively. In order to realize the classification of large-scale ciphertext data in distributed servers, Yang et al [34] proposed a vector homomorphic encryption (VHE) scheme through constructing key switching matrix and noise matrix and constructed a secure distributed KNN classification algorithm (seed KNN) based on it. Liu et al [36] achieved secure KNN classification by a secure and efficient query processing (SecEQP) scheme, which encodes location information through a projection function and implements locationbased query processing based on the encrypted geospatial data stored in the cloud

Preliminaries

System Models

System Algorithm

Security Analysis

Experimental Test

Conclusions