Towards Multi-User, Secure, and Verifiable kNN Query in Cloud Database

Article


Cui, Ningning, Qian, Kang, Cai, Taotao, Li, Jianxin, Yang, Xiaochun, Cui, Jie and Zhong, Hong. 2023. "Towards Multi-User, Secure, and Verifiable kNN Query in Cloud Database." IEEE Transactions on Knowledge and Data Engineering. 35 (9), pp. 9333-9349. https://doi.org/10.1109/TKDE.2023.3237879
Article Title

Towards Multi-User, Secure, and Verifiable kNN Query in Cloud Database

ERA Journal ID17876
Article CategoryArticle
AuthorsCui, Ningning, Qian, Kang, Cai, Taotao, Li, Jianxin, Yang, Xiaochun, Cui, Jie and Zhong, Hong
Journal TitleIEEE Transactions on Knowledge and Data Engineering
Journal Citation35 (9), pp. 9333-9349
Number of Pages17
Year2023
PublisherIEEE (Institute of Electrical and Electronics Engineers)
Place of PublicationUnited States
ISSN1041-4347
1558-2191
Digital Object Identifier (DOI)https://doi.org/10.1109/TKDE.2023.3237879
Web Address (URL)https://ieeexplore.ieee.org/document/10023987
Abstract

With 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 about data security and result integrity have become on the rise sharply. In addition, in the single-user situation assumed by most existing works, query users can capture query content from each other even though the queries are encrypted, which may incur the leakage of query privacy. Unfortunately, there exists little work that can commendably assure data security and result integrity in the multi-user setting. To this end, in this article, we study the problem of multi-user, secure, and verifiable k nearest neighbor query ( MSV kk NN ). To support MSV k NN, 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 MSV k NN query, our proposed solution can not merely answer the query efficiently while can guarantee: 1) preserving data privacy , query privacy , result privacy , and access patterns privacy ; 2) authenticating the correctness and completeness of the results; 3) supporting multi-user with different keys. Finally, the formal security analysis and complexity analysis are theoretically proven and the performance and feasibility of our proposed approach are empirically evaluated and demonstrated.

KeywordsData outsourcing; result verification; privacypreserving; kNN query; multiple users
Related Output
Is supplemented byhttps://dataportal.arc.gov.au/NCGP/Web/Grant/Grant/LP180100750
Contains Sensitive ContentDoes not contain sensitive content
ANZSRC Field of Research 2020460599. Data management and data science not elsewhere classified
Public NotesFiles associated with this item cannot be displayed due to copyright restrictions.
Byline AffiliationsAnhui University, China
Macquarie University
Deakin University
Northeastern University, China
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