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Many graph mining and analysis services have been deployed on the cloud,which can alleviate users from the burden of implementing and maintaining graphalgorithms. However, putting graph analytics on the cloud can invade users'privacy. To solve this problem, we propose CryptGraph, which runs graphanalytics on encrypted graph to preserve the privacy of both users' graph dataand the analytic results. In CryptGraph, users encrypt their graphs beforeuploading them to the cloud. The cloud runs graph analysis on the encryptedgraphs and obtains results which are also in encrypted form that the cloudcannot decipher. During the process of computing, the encrypted graphs arenever decrypted on the cloud side. The encrypted results are sent back to usersand users perform the decryption to obtain the plaintext results. In thisprocess, users' graphs and the analytics results are both encrypted and thecloud knows neither of them. Thereby, users' privacy can be strongly protected.Meanwhile, with the help of homomorphic encryption, the results analyzed fromthe encrypted graphs are guaranteed to be correct. In this paper, we presenthow to encrypt a graph using homomorphic encryption and how to query thestructure of an encrypted graph by computing polynomials. To solve the problemthat certain operations are not executable on encrypted graphs, we propose hardcomputation outsourcing to seek help from users. Using two graph algorithms asexamples, we show how to apply our methods to perform analytics on encryptedgraphs. Experiments on two datasets demonstrate the correctness and feasibilityof our methods.

CryptGraph: Privacy Preserving Graph Analytics on Encrypted Graph