CRYPTO '89 Proceedings of the 9th Annual International Cryptology Conference on Advances in Cryptology
Privacy preserving association rule mining in vertically partitioned data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Authentic data publication over the internet
Journal of Computer Security - IFIP 2000
Integrity auditing of outsourced data
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Verifying Completeness of Relational Query Answers from Online Servers
ACM Transactions on Information and System Security (TISSEC)
A new efficient privacy-preserving scalar product protocol
AusDM '07 Proceedings of the sixth Australasian conference on Data mining and analytics - Volume 70
Dual encryption for query integrity assurance
Proceedings of the 17th ACM conference on Information and knowledge management
Authenticated indexing for outsourced spatial databases
The VLDB Journal — The International Journal on Very Large Data Bases
Impossibility of unconditionally secure scalar products
Data & Knowledge Engineering
Efficient privacy-preserving similar document detection
The VLDB Journal — The International Journal on Very Large Data Bases
Authenticated Index Structures for Aggregation Queries
ACM Transactions on Information and System Security (TISSEC)
Authenticated Multistep Nearest Neighbor Search
IEEE Transactions on Knowledge and Data Engineering
On private scalar product computation for privacy-preserving data mining
ICISC'04 Proceedings of the 7th international conference on Information Security and Cryptology
Computing on authenticated data
TCC'12 Proceedings of the 9th international conference on Theory of Cryptography
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Inner product computation of vectors has been extensively applied in a number of computer application fields. Many applications with inner product of vectors as the fundamental operation have been deployed on the cloud computing platform. As the service provider of the cloud computing may not be completely trustworthy, it is necessary for the client to verify the correctness of the returned computation results of inner product of vectors. In this paper, we present an effective and efficient correctness verification mechanism for the inner product of vectors, which is named aggregate verification vector and constructed on the algebraic properties of the inner product of vectors. The aggregate verification vector is constructed secretly by the data owner, shared with the client, and kept secretly from the service provider. On the basis of the aggregate verification vector, we propose a novel verification scheme, which enables the client to check whether the returned computation results are correct or not. We make exhaustive security analysis of the proposed verification scheme and show that the scheme provides strong probabilistic guarantees on the correctness of the computation results of inner product between vectors. Extensive experiments demonstrate the performance efficiency of our proposed verification scheme.