Privacy-assured substructure similarity query over encrypted graph-structured data in cloud

Abstract

In recent years, large amounts of graph-structured data have been outsourced to the commercial public cloud. It is a crucial requirement to enable substructure similarity query for effective data retrieval. However, for protecting data privacy, sensitive data have to be encrypted before outsourcing, which impedes the traditional similarity query schemes from being supported in cloud. Most existing works on encrypted cloud data retrieval pay little attention to this problem. Additionally, considering the huge amounts of encrypted data graphs, the complicated similarity computation and privacy requirements, it is particularly challenging to solve this problem effectively. In this paper, for the first time, we investigate the problem of privacy-assured substructure similarity query over encrypted graph-structured data in cloud computing. Our solution explores a secure framework and a series of secure algorithms to efficiently perform the substructure similarity query without privacy breaches. The proposed solution first builds a secure feature-graph index to represent the feature-related information about each encrypted data graph based on privacy homomorphism and obscuration methods and then calculates the similarity between the query graph and each data graph by the difference of feature frequency in a privacy-preserving manner. Thorough analysis is given to investigate effectiveness and privacy guarantees, and the experiments with real dataset further demonstrate the validity and efficiency of the proposed solution. Copyright © 2013 John Wiley & Sons, Ltd.