Towards Multi-User, Secure, and Verifiable kNN Query in Cloud Database
Article
Article Title | Towards Multi-User, Secure, and Verifiable kNN Query in Cloud Database |
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ERA Journal ID | 17876 |
Article Category | Article |
Authors | Cui, Ningning, Qian, Kang, Cai, Taotao, Li, Jianxin, Yang, Xiaochun, Cui, Jie and Zhong, Hong |
Journal Title | IEEE Transactions on Knowledge and Data Engineering |
Journal Citation | 35 (9), pp. 9333-9349 |
Number of Pages | 17 |
Year | 2023 |
Publisher | IEEE (Institute of Electrical and Electronics Engineers) |
Place of Publication | United States |
ISSN | 1041-4347 |
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. |
Keywords | Data outsourcing; result verification; privacypreserving; kNN query; multiple users |
Related Output | |
Is supplemented by | https://dataportal.arc.gov.au/NCGP/Web/Grant/Grant/LP180100750 |
Contains Sensitive Content | Does not contain sensitive content |
ANZSRC Field of Research 2020 | 460599. Data management and data science not elsewhere classified |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | Anhui University, China |
Macquarie University | |
Deakin University | |
Northeastern University, China |
https://research.usq.edu.au/item/z6031/towards-multi-user-secure-and-verifiable-knn-query-in-cloud-database
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