一种云环境下图数据中带边权重的隐私保护方法

Anedge weight information privacy protection for graph data in cloud circumstance

云服务器端的数据始终面临着巨大的安全威胁。提出一种云环境下图数据中带边权重的隐私保护方法 HEPP-GD(Homomorphic Encryption for Privacy Protect in Graph Data)。HEPP-GD采用Paillier同态加密体系对图数据的边权重进行加密,在云服务器端计算图数据中顶点之间的最短距离,这样边权重隐私信息将不会被非法的获取,本地客户端保存自己的密钥使得加密后的信息在因特网上传输。建立了HEPP-GD实验环境.测试结果表明,在云服务器端的大规模的图数据情况下,HEPP-GD隐私保护方法可以利用很少的内存资源完成最短路径距离的计算,其安全性通过Paillier加密得到了保证。

The data security problems existed in cloud server for some malicious attacks.An edge weight information privacy protection for graph data in cloud circumstance called HEPP-GD(Homomorphic Encryption for Privacy Protect for Graph Data)was proposed and described in this paper.HEPP-GD used homomorphic encryption artitecture to encrypt graph data in the cloud platform.The shortest distance was calculated so that the real edge weight information will not be illegally obtained by the cloud service provider or unauthorized users,which effectively guarantee the security of the user s privacy.The key is held by the user and the encrypted edge weight information was transmitted over the internet securely.The experiments results and performance analysis show that HEPP-GD can effectively reduce the memory resource cost when calculating shortest distance between nodes in large scale graph data.The graph data is not attacked and the privacy of information is security using Paillier encryption algorithm.