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SVkNN: efficient secure and verifiable k-nearest neighbor query on the cloud platform *
conference contribution
posted on 2020-01-01, 00:00 authored by Ningning Cui, Xiaochun Yang, Bin Wang, Jianxin LiJianxin Li, Guoren WangWith the boom in cloud computing, data outsourcing in location-based services is proliferating and has attracted increasing interest from research communities and commercial applications. Nevertheless, since the cloud server is probably both untrusted and malicious, concerns of data security and result integrity have become on the rise sharply. However, there exist little work that can commendably assure the data security and result integrity using a unified way. In this paper, we study the problem of secure and verifiable k nearest neighbor query (SVkNN). To support SVkNN, we first propose a novel unified structure, called verifiable and secure index (VSI). Based on this, we devise a series of secure protocols to facilitate query processing and develop a compact verification strategy. Given an SVkNN query, our proposed solution can not merely answer the query efficiently while can guarantee: 1) preserving the privacy of data, query, result and access patterns; 2) authenticating the correctness and completeness of the results without leaking the confidentiality. Finally, the formal security analysis and complexity analysis are theoretically proven and the performance and feasibility of our proposed approaches are empirically evaluated and demonstrated.
History
Event
Data Engineering. International Conference (36th : 2020 : Dallas, Tex.)Pagination
253 - 264Publisher
IEEELocation
Dallas, Tex.Place of publication
Piscataway, N.J.Publisher DOI
Start date
2020-04-20End date
2020-04-24ISSN
1063-6382eISSN
2375-026XISBN-13
9781728129037Language
engPublication classification
E1 Full written paper - refereedEditor/Contributor(s)
UnknownTitle of proceedings
ICDE 2020 : Proceedings of the IEEE 36th International Conference on Data EngineeringUsage metrics
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