$k$NN Query in Cloud Database | IEEE Transactions on Knowledge and Data Engineering"/>
  Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
skip to main content
research-article

Towards Multi-User, Secure, and Verifiable <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula>NN Query in Cloud Database

Published: 01 September 2023 Publication History

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 <inline-formula><tex-math notation="LaTeX">$k$</tex-math><alternatives><mml:math><mml:mi>k</mml:mi></mml:math><inline-graphic xlink:href="cui-ieq2-3237879.gif"/></alternatives></inline-formula> nearest neighbor query (<bold>MSV <inline-formula><tex-math notation="LaTeX">$k$</tex-math><alternatives><mml:math><mml:mi>k</mml:mi></mml:math><inline-graphic xlink:href="cui-ieq3-3237879.gif"/></alternatives></inline-formula> NN</bold>). To support MSV <inline-formula><tex-math notation="LaTeX">$k$</tex-math><alternatives><mml:math><mml:mi>k</mml:mi></mml:math><inline-graphic xlink:href="cui-ieq4-3237879.gif"/></alternatives></inline-formula> 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 <inline-formula><tex-math notation="LaTeX">$k$</tex-math><alternatives><mml:math><mml:mi>k</mml:mi></mml:math><inline-graphic xlink:href="cui-ieq5-3237879.gif"/></alternatives></inline-formula> NN query, our proposed solution can not merely answer the query efficiently while can guarantee: 1) preserving <italic>data privacy</italic>, <italic>query privacy</italic>, <italic>result privacy</italic>, and <italic>access patterns privacy</italic>; 2) authenticating the <italic>correctness</italic> and <italic>completeness</italic> of the results; 3) supporting <italic>multi-user</italic> 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.

References

[1]
S. Wu, Q. Li, G. Li, D. Yuan, X. Yuan, and C. Wang, “ServeDB: Secure, verifiable, and efficient range queries on outsourced database,” in Proc. IEEE Int. Conf. Data Eng., 2019, pp. 626–637.
[2]
X. Lei, A. X. Liu, R. Li, and G. Tu, “SecEQP: A secure and efficient scheme for kNN query problem over encrypted geodata on cloud,” in Proc. IEEE Int. Conf. Data Eng., 2019, pp. 662–673.
[3]
W. K. Wong, D. W. Cheung, B. Kao, and N. Mamoulis, “Secure kNN computation on encrypted databases,” in Proc. ACM Int. Conf. Manage. Data, 2009, pp. 139–152.
[4]
Y. Zhu, R. Xu, and T. Takagi, “Secure k-NN computation on encrypted cloud data without sharing key with query users,” in Proc. ACM SIGSAC Conf. Comput. Commun. Secur., 2013, pp. 55–60.
[5]
S. Choi, G. Ghinita, H. S. Lim, and E. Bertino, “Secure kNN query processing in untrusted cloud environments,” IEEE Trans. Knowl. Data Eng., vol. 26, no. 11, pp. 2818–2831, Nov. 2014.
[6]
R. A. Popa, F. H. Li, and N. Zeldovich, “An ideal-security protocol for order-preserving encoding,” in Proc. IEEE Int. Conf. Secur. Privacy, 2013, pp. 463–477.
[7]
G. Ghinita, P. Kalnis, A. Khoshgozaran, C. Shahabi, and K. Tan, “Private queries in location based services: Anonymizers are not necessary,” in Proc. ACM Int. Conf. Manage. Data, 2008, pp. 121–132.
[8]
P. Williams, R. Sion, and B. Carbunar, “Building castles out of mud: Practical access pattern privacy and correctness on untrusted storage,” in Proc. ACM SIGSAC Conf. Comput. Commun. Secur., 2008, pp. 139–148.
[9]
M. S. Islam, M. Kuzu, and M. Kantarcioglu, “Access pattern disclosure on searchable encryption: Ramification, attack and mitigation,” in Proc. Int. Conf. Netw. Distrib. Syst. Secur. Symp., 2012, pp. 1–15.
[10]
Y. Elmehdwi, B. K. Samanthula, and W. Jiang, “Secure k-nearest neighbor query over encrypted data in outsourced environments,” in Proc. IEEE Int. Conf. Data Eng., 2014, pp. 664–675.
[11]
H. I. Kim, H. J. Kim, and J. W. Chang, “A secure kNN query processing algorithm using homomorphic encryption on outsourced database,” Data Knowl. Eng., vol. 123, 2019, Art. no.
[12]
R. Li, A. X. Liu, H. Xu, Y. Liu, and H. Yuan, “Adaptive secure nearest neighbor query processing over encrypted data,” IEEE Trans. Dependable Secure Comput., vol. 19, no. 1, pp. 91–106, Jan./Feb. 2022.
[13]
B. Wang, Y. Hou, and M. Li, “QuickN: Practical and secure nearest neighbor search on encrypted large-scale data,” IEEE Trans. Cloud Comput., vol. 10, no. 3, pp. 2066–2078, Third Quarter 2022.
[14]
X. Lei, G. -H. Tu, A. X. Liu, and T. Xie, “Fast and secure kNN query processing in cloud computing,” in Proc. IEEE Conf. Commun. Netw. Secur., 2020, pp. 1–9.
[15]
A. Liu, K. Zheng, L. Li, G. Liu, L. Zhao, and X. Zhou, “Efficient secure similarity computation on encrypted trajectory data,” in Proc. IEEE Int. Conf. Data Eng., 2015, pp. 66–77.
[16]
P. Paillier, “Public-key cryptosystems based on composite degree residuosity classes,” in Proc. Int. Conf. Theory Appl. Cryptographic Techn., 1999, pp. 223–238.
[17]
A. C. Yao, “How to generate and exchange secrets (extended abstract),” in Proc. IEEE Int. Conf. Found. Comput. Sci., 1986, pp. 162–167.
[18]
H. Rong, H. Wang, J. Liu, W. Wu, and M. Xian, “Efficient integrity verification of secure outsourced kNN computation in cloud environments,” in Proc. IEEE Int. Conf. Trustcom, 2016, pp. 236–243.
[19]
B. Yao, F. Li, and X. Xiao, “Secure nearest neighbor revisited,” in Proc. IEEE Int. Conf. Data Eng., 2013, pp. 733–744.
[20]
B. Wang, Y. Hou, and M. Li, “Practical and secure nearest neighbor search on encrypted large-scale data,” in Proc. IEEE Conf. Comput. Commun., 2016, pp. 1–9.
[21]
X. Yi, R. Paulet, E. Bertino, and V. Varadharajan, “Practical approximate k nearest neighbor queries with location and query privacy,” IEEE Trans. Knowl. Data Eng., vol. 28, no. 6, pp. 1546–1559, Jun. 2016.
[22]
R. C. Merkle, “A certified digital signature,” in Proc. Int. Conf. Theory Appl. Cryptology, 1989, pp. 218–238.
[23]
Y. Yang, S. Papadopoulos, D. Papadias, and G. Kollios, “Authenticated indexing for outsourced spatial databases,” in Proc. Int. Conf. Very Large Data Bases, 2009, pp. 631–648.
[24]
M. L. Yiu, E. Lo, and D. Yung, “Authentication of moving kNN queries,” in Proc. IEEE Int. Conf. Data Eng., 2011, pp. 565–576.
[25]
Q. Chen, H. Hu, and J. Xu, “Authenticating top-k queries in location-based services with confidentiality,” in Proc. Int. Conf. Very Large Data Bases, 2013, pp. 49–60.
[26]
X. Lin, J. Xu, and H. Hu, “Authentication of location-based skyline queries,” in Proc. ACM Int. Conf. Inf. Knowl. Manage., 2011, pp. 1583–1588.
[27]
H. Pang, A. Jain, K. Ramamritham, and K. Tan, “Verifying completeness of relational query results in data publishing,” in Proc. ACM Int. Conf. Manage. Data, 2005, pp. 407–418.
[28]
J. Liu, J. Yang, L. Xiong, and J. Pei, “Secure skyline queries on cloud platform,” in Proc. IEEE Int. Conf. Data Eng., 2017, pp. 633–644.
[29]
C. Xu, J. Xu, H. Hu, and M. H. Au, “When query authentication meets fine-grained access control: A zero-knowledge approach,” in Proc. ACM Int. Conf. Manage. Data, 2018, pp. 147–162.
[30]
N. Cui, X. Yang, B. Wang, J. Li, and G. Wang, “SVkNN: Efficient secure and verifiable k-nearest neighbor query on the cloud platform,” in Proc. IEEE Int. Conf. Data Eng., 2020, pp. 253–264.
[31]
F. Song, Z. Qin, J. Liang, and X. Lin, “An efficient and privacy-preserving multi-user multi-keyword search scheme without key sharing,” in Proc. IEEE Int. Cryptol. Conf., 2021, pp. 1–6.
[32]
S. Han, D. Shen, T. Nie, Y. Kou, and G. Yu, “Private blocking technique for multi-party privacy-preserving record linkage,” Data Sci. Eng., vol. 2, pp. 187–196, 2017.
[33]
K. Cheng et al., “Secure k-NN query on encrypted cloud data with multiple keys,” IEEE Trans. Big Data, vol. 7, no. 4, pp. 689–702, Oct. 2021.
[34]
K. Cheng, Y. Shen, Y. Wang, L. Wang, and J. Ma, “Strongly secure and efficient range queries in cloud databases under multiple keys,” in Proc. IEEE Conf. Comput. Commun., 2019, pp. 2494–2502.
[35]
X. Liu, H. D. Robert, and R. C. Kim-Kwang, “An efficient privacy-preserving outsourced calculation toolkit with multiple keys,” IEEE Trans. Inf. Forensics Secur., vol. 11, no. 11, pp. 2401–2414, Nov. 2016.
[36]
S. K. Nayak and S. Tripathy, “SEMKC: Secure and efficient computation over outsourced data encrypted under multiple keys,” IEEE Trans. Emerg. Topics Comput., vol. 9, no. 1, pp. 414–428, First Quarter 2021.
[37]
X. Liu, G. Yang, Y. Mu, and R. H. Deng, “Multi-user verifiable searchable symmetric encryption for cloud storage,” IEEE Trans. Dependable Secure Comput., vol. 17, no. 6, pp. 1322–1332, Nov./Dec. 2020.
[38]
Y. Yang, X. Liu, and R. H. Deng, “Multi-user multi-keyword rank search over encrypted data in arbitrary language,” IEEE Trans. Dependable Secure Comput., vol. 17, no. 2, pp. 320–334, Mar./Apr. 2020.
[39]
X. Ding, Z. Wang, P. Zhou, K. -K. R. Choo, and H. Jin, “Efficient and privacy-preserving multi-party skyline queries over encrypted data,” IEEE Trans. Inf. Forensics Secur., vol. 16, pp. 4589–4604, Aug. 2021.
[40]
X. Yu, Y. Hu, R. Zhang, Z. Yan, and Y. Zhang, “Secure outsourced top-k selection queries against untrusted cloud service providers, in Proc. IEEE/ACM 29th Int. Symp. Qual. Serv., 2021, pp. 1–10.

Cited By

View all
  • (2024)Secure Similarity Queries Over Vertically Distributed Data via TEE-Enhanced Cloud ComputingIEEE Transactions on Information Forensics and Security10.1109/TIFS.2024.341363019(6237-6251)Online publication date: 1-Jan-2024
  • (2023)Comprehensive Survey on Privacy-Preserving Spatial Data Query in Transportation SystemsIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2023.329579824:12(13603-13616)Online publication date: 1-Dec-2023

Recommendations

Comments

Information & Contributors

Information

Published In

cover image IEEE Transactions on Knowledge and Data Engineering
IEEE Transactions on Knowledge and Data Engineering  Volume 35, Issue 9
Sept. 2023
1110 pages

Publisher

IEEE Educational Activities Department

United States

Publication History

Published: 01 September 2023

Qualifiers

  • Research-article

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)0
  • Downloads (Last 6 weeks)0
Reflects downloads up to 20 Jan 2025

Other Metrics

Citations

Cited By

View all
  • (2024)Secure Similarity Queries Over Vertically Distributed Data via TEE-Enhanced Cloud ComputingIEEE Transactions on Information Forensics and Security10.1109/TIFS.2024.341363019(6237-6251)Online publication date: 1-Jan-2024
  • (2023)Comprehensive Survey on Privacy-Preserving Spatial Data Query in Transportation SystemsIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2023.329579824:12(13603-13616)Online publication date: 1-Dec-2023

View Options

View options

Media

Figures

Other

Tables

Share

Share

Share this Publication link

Share on social media