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Issue No. 04 - April (2018 vol. 30)
ISSN: 1041-4347
pp: 630-644
Cheng Xu , Department of Computer Science, Hong Kong Baptist University, Hong Kong
Qian Chen , Department of Computer Science, Hong Kong Baptist University, Hong Kong
Haibo Hu , Department of Electronic and Information Engineering, Hong Kong Polytechnic University, Hong Kong
Jianliang Xu , Department of Computer Science, Hong Kong Baptist University, Hong Kong
Xiaojun Hei , School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan, China
ABSTRACT
With recent advances in data-as-a-service (DaaS) and cloud computing, aggregate query services over set-valued data are becoming widely available for business intelligence that drives decision making. However, as the service provider is often a third-party delegate of the data owner, the integrity of the query results cannot be guaranteed and is thus imperative to be authenticated. Unfortunately, existing query authentication techniques either do not work for set-valued data or they lack data confidentiality. In this paper, we propose authenticated aggregate queries over set-valued data that not only ensure the integrity of query results but also preserve the confidentiality of source data. As many aggregate queries are composed of multiset operations such as set union and subset, we first develop a family of privacy-preserving authentication protocols for primitive multiset operations. Using these protocols as building blocks, we present a privacy-preserving authentication framework for various aggregate queries and further optimize their authentication performance. Security analysis and empirical evaluation show that our proposed privacy-preserving authentication techniques are feasible and robust under a wide range of system workloads.
INDEX TERMS
Aggregates, Authentication, Genomics, Bioinformatics, Protocols, Indexes, Query processing
CITATION

C. Xu, Q. Chen, H. Hu, J. Xu and X. Hei, "Authenticating Aggregate Queries over Set-Valued Data with Confidentiality," in IEEE Transactions on Knowledge & Data Engineering, vol. 30, no. 4, pp. 630-644, 2018.
doi:10.1109/TKDE.2017.2773541
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