Privacy-Protection Path Finding Supporting the Ranked Order on Encrypted Graph in Big Data Environment

• Published in: IEEE access Vol. 8; pp. 214596 - 214604
• Main Authors: Wu, Bin, Chen, Xianyi, Zhang, Caicai, Mei, Zhuolin, Zhao, Zhiqiang, Wu, Zongda, Yan, Tao
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Big Data; Data privacy; encrypted graph; Encryption; Indexes; Outsourcing; Path finding; Privacy; ranked order; searchable encryption; Security; Servers
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2020.3040781

Since data outsourcing in big data environment become popular and become a trend, large quantities of graph data are outsourced to the big data server for saving cost. As the big data server can not be fully reliable, people usually encrypt the graph data before they are outsourced to the big data platform for the privacy protection. The path finding is a frequently-used action and can be useful for production and living. The path finding supporting the ranked order is a more useful operation, and a user can obtain a ranked search result set. Because of the outsourced graph data being encrypted on the big data server, the path finding supporting the ranked order becomes a task with enough challenge. In this paper, we propose a solution to perform privacy-protection path finding supporting the ranked order on encrypted graph in big data environment (PPFR). Our research uses an encryption mechanism and a ranking strategy to achieve path finding supporting the ranked order. We formally analyze the security of our scheme. We demonstrate the efficiency of our proposed scheme on a real graph data set by experiment.