CryptGraph: Privacy Preserving Graph Analytics on Encrypted Graph

TL;DR

CryptGraph enables privacy-preserving graph analytics on encrypted data using homomorphic encryption, allowing cloud-based analysis without exposing users' graph data or results, ensuring privacy and correctness.

Contribution

This paper introduces CryptGraph, a novel framework that performs graph analytics on encrypted graphs using homomorphic encryption, with methods for encryption, querying, and outsourcing computations.

Findings

Encrypted graph analysis is feasible and accurate.

CryptGraph preserves user privacy during cloud-based graph analytics.

Experimental results validate the correctness and practicality of the approach.

Abstract

Many graph mining and analysis services have been deployed on the cloud, which can alleviate users from the burden of implementing and maintaining graph algorithms. However, putting graph analytics on the cloud can invade users' privacy. To solve this problem, we propose CryptGraph, which runs graph analytics on encrypted graph to preserve the privacy of both users' graph data and the analytic results. In CryptGraph, users encrypt their graphs before uploading them to the cloud. The cloud runs graph analysis on the encrypted graphs and obtains results which are also in encrypted form that the cloud cannot decipher. During the process of computing, the encrypted graphs are never decrypted on the cloud side. The encrypted results are sent back to users and users perform the decryption to obtain the plaintext results. In this process, users' graphs and the analytics results are both encrypted and the cloud knows neither of them. Thereby, users' privacy can be strongly protected. Meanwhile, with the help of homomorphic encryption, the results analyzed from the encrypted graphs are guaranteed to be correct. In this paper, we present how to encrypt a graph using homomorphic encryption and how to query the structure of an encrypted graph by computing polynomials. To solve the problem that certain operations are not executable on encrypted graphs, we propose hard computation outsourcing to seek help from users. Using two graph algorithms as examples, we show how to apply our methods to perform analytics on encrypted graphs. Experiments on two datasets demonstrate the correctness and feasibility of our methods.