Heterogeneous secure multi-party computation
IFIP'12 Proceedings of the 11th international IFIP TC 6 conference on Networking - Volume Part II
Small business-oriented index construction of cloud data
ICA3PP'12 Proceedings of the 12th international conference on Algorithms and Architectures for Parallel Processing - Volume Part II
Efficient tree pattern queries on encrypted XML documents
Proceedings of the Joint EDBT/ICDT 2013 Workshops
Future Generation Computer Systems
Secure k-NN computation on encrypted cloud data without sharing key with query users
Proceedings of the 2013 international workshop on Security in cloud computing
Secure k-NN query on encrypted cloud database without key-sharing
International Journal of Electronic Security and Digital Forensics
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In the emerging cloud computing paradigm, data owners become increasingly motivated to outsource their complex data management systems from local sites to the commercial public cloud for great flexibility and economic savings. For the consideration of users' privacy, sensitive data have to be encrypted before outsourcing, which makes effective data utilization a very challenging task. In this paper, for the first time, we define and solve the problem of privacy-preserving query over encrypted graph-structured data in cloud computing (PPGQ), and establish a set of strict privacy requirements for such a secure cloud data utilization system to become a reality. Our work utilizes the principle of "filtering-and-verification". We prebuild a feature-based index to provide feature-related information about each encrypted data graph, and then choose the efficient inner product as the pruning tool to carry out the filtering procedure. To meet the challenge of supporting graph query without privacy breaches, we propose a secure inner product computation technique, and then improve it to achieve various privacy requirements under the known-background threat model.