Skip to main content

Advertisement

Springer Nature Link
Log in

Query on the cloud: improved privacy-preserving k-nearest neighbor classification over the outsourced database

  • Published:
World Wide Web Aims and scope Submit manuscript

Abstract

In the current cloud computing and big data era, outsourcing the storage and associated query operations of large-scale databases to cloud service providers has become an increasingly popular computing paradigm. However, due to the potential mutual distrust among the data owner (DO), cloud server (CS) and query user (QU), the risk of privacy disclosure constrains the wide deployment of this attractive computing paradigm. To handle this dilemma, various encryption approaches are designed to assure privacy during query processing over outsourced databases. Recently, Wu et al. (World Wide Web 22(1), 101–123 2019) presented a ‘secure’ k-nearest neighbor (k NN) classification scheme over encrypted cloud database which aimed to concurrently keep the privacy of the database, the DO’s key, the QU’s query, and the data access patterns. In this paper, we first revisit their scheme and present an efficient known-plaintext attack on the privacy of database with linearization technique. Then, we propose an improved scheme that outperforms the prior scheme in both security and efficiency. Precisely, on the security side, we realize the above four security intentions with rigorous theoretical arguments. On the efficiency side, our new design greatly reduces the computational overhead of the DO and the QU, and makes full use of the resources of two cloud servers by better balancing their computational loads. Finally, we measure the practical performance of our scheme from an experimental perspective, and the result corroborates our theoretical analysis.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Algorithm 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9

Similar content being viewed by others

References

  1. Wu, W., Parampalli, U., Liu, J., Xian, M.: Privacy preserving k-nearest neighbor classification over encrypted database in outsourced cloud environments. World Wide Web 22(1), 101–123 (2019)

    Article  Google Scholar 

  2. Tian, C., Yu, J., Zhang, H., Xue, H., Wang, C., Ren, K.: Novel secure outsourcing of modular inversion for arbitrary and variable modulus. IEEE Trans. Serv. Comput. 15(1), 241–253 (2022). https://doi.org/10.1109/TSC.2019.2937486

    Article  Google Scholar 

  3. Zhang, H., Gao, P., Yu, J., Lin, J., Xiong, N.: Machine learning on cloud with blockchain: a secure, verifiable and fair approach to outsource the linear regression for data analysis. IEEE Transactions on Network Science and Engineering, 1–1. https://doi.org/10.1109/TNSE.2021.3110101 (2021)

  4. Ge, Y.-F., Yu, W.-J., Cao, J., Wang, H., Zhan, Z.-H., Zhang, Y., Zhang, J.: Distributed memetic algorithm for outsourced database fragmentation. IEEE Trans. Cybern. 51(10), 4808–4821 (2021). https://doi.org/10.1109/TCYB.2020.3027962

    Article  Google Scholar 

  5. Vimalachandran, P., Liu, H., Lin, Y., Ji, K., Wang, H., Zhang, Y.: Improving accessibility of the australian my health records while preserving privacy and security of the system. Health Inf. Sci. Syst. 8(1), 1–9 (2020)

    Article  Google Scholar 

  6. Zhang, J., Li, H., Liu, X., Luo, Y., Chen, F., Wang, H., Chang, L.: On efficient and robust anonymization for privacy protection on massive streaming categorical information. IEEE Trans. Dependable Secure Comput. 14(5), 507–520 (2015)

    Article  Google Scholar 

  7. Wang, H., Sun, L., Bertino, E.: Building access control policy model for privacy preserving and testing policy conflicting problems. J. Comput. Syst. Sci. 80(8), 1493–1503 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  8. Wu, W., Liu, J., Wang, H., Hao, J., Xian, M.: Secure and efficient outsourced k-means clustering using fully homomorphic encryption with ciphertext packing technique. IEEE Trans. Knowl. Data Eng. 33(10), 3424–3437 (2021). https://doi.org/10.1109/TKDE.2020.2969633

    Article  Google Scholar 

  9. Wu, W., Xian, M., Parampalli, U., Lu, B.: Efficient privacy-preserving frequent itemset query over semantically secure encrypted cloud database. World Wide Web 24(2), 607–629 (2021)

    Article  Google Scholar 

  10. Wang, H., Wang, Y., Taleb, T., Jiang, X.: Special issue on security and privacy in network computing. World Wide Web 23(2), 951–957 (2020)

    Article  Google Scholar 

  11. Wang, J., Chen, X.: Efficient and secure storage for outsourced data: a survey. Data Sci. Eng. 1(3), 178–188 (2016)

    Article  Google Scholar 

  12. Cui, N., Yang, X., Wang, B., Geng, J., Li, J.: Secure range query over encrypted data in outsourced environments. World Wide Web 23(1), 491–517 (2020)

    Article  Google Scholar 

  13. Fix, E., Hodges, J. L.: Nonparametric discrimination: Consistency properties. Randolph Field, Texas Project 21–49 (1951)

  14. Cao, N., Wang, C., Li, M., Ren, K., Lou, W.: Privacy-preserving multi-keyword ranked search over encrypted cloud data. IEEE Trans. Parallel Distrib. Syst. 25(1), 222–233 (2013)

    Article  Google Scholar 

  15. Garcia-Teodoro, P., Diaz-Verdejo, J., Maciá-Fernández, G., Vázquez, E.: Anomaly-based network intrusion detection: techniques, systems and challenges. Comput. Secur. 28(1-2), 18–28 (2009)

    Article  Google Scholar 

  16. Su, M. -Y.: Using clustering to improve the k nn-based classifiers for online anomaly network traffic identification. J. Netw. Comput. Appl. 34(2), 722–730 (2011)

    Article  MathSciNet  Google Scholar 

  17. Park, J., Lee, D. H.: Privacy preserving k-nearest neighbor for medical diagnosis in e-health cloud. Journal of healthcare Engineering, 2018 (2018)

  18. Zhu, D., Zhu, H., Liu, X., Li, H., Wang, F., Li, H.: Achieve efficient and privacy-preserving medical primary diagnosis based on k nn. In: 2018 27th International Conference on Computer Communication and Networks (ICCCN), pp. 1–9. IEEE (2018)

  19. Wang, B., Liao, Q., Zhang, C.: Weight based k nn recommender system. In: 2013 5th International Conference on Intelligent Human-Machine Systems and Cybernetics, vol. 2, pp. 449–452. IEEE (2013)

  20. Wong, W. K., Cheung, D.W.-L., Kao, B., Mamoulis, N.: Secure k nn computation on encrypted databases. In: Proceedings of the 2009 ACM SIGMOD International Conference on Management of Data, pp. 139–152 (2009)

  21. Yao, B., Li, F., Xiao, X.: Secure nearest neighbor revisited. In: 2013 IEEE 29th International Conference on Data Engineering (ICDE), pp. 733–744. IEEE (2013)

  22. Yiu, M. L., Assent, I., Jensen, C. S., Kalnis, P.: Outsourced similarity search on metric data assets. IEEE Trans. Knowl. Data Eng. 24(2), 338–352 (2010)

    Article  Google Scholar 

  23. Elmehdwi, Y., Samanthula, B.K., Jiang, W.: Secure k-nearest neighbor query over encrypted data in outsourced environments. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 664–675. IEEE (2014)

  24. Xu, H., Guo, S., Chen, K.: Building confidential and efficient query services in the cloud with rasp data perturbation. IEEE Trans. Knowledge Data Eng. 26(2), 322–335 (2012)

    Google Scholar 

  25. Rong, H., Wang, H., Liu, J., Wu, W., Xian, M.: Efficient integrity verification of secure outsourced k nn computation in cloud environments. In: 2016 IEEE Trustcom/BigDataSE/ISPA, pp. 236–243. IEEE (2016)

  26. Zhu, Y., Xu, R., Takagi, T.: Secure k-nn computation on encrypted cloud data without sharing key with query users. In: Proceedings of the 2013 International Workshop on Security in Cloud Computing, pp. 55–60 (2013)

  27. Zhu, Y., Huang, Z., Takagi, T.: Secure and controllable k-nn query over encrypted cloud data with key confidentiality. J. Parallel Distrib. Comput. 89, 1–12 (2016)

    Article  Google Scholar 

  28. Cui, N., Yang, X., Wang, B., Li, J., Wang, G.: SvK Nn: Efficient secure and verifiable K-nearest neighbor query on the cloud platform. In: 2020 IEEE 36Th International Conference on Data Engineering (ICDE), pp. 253–264. https://doi.org/10.1109/ICDE48307.2020.00029 (2020)

  29. Lei, X., Liu, A.X., Li, R., Tu, G.-H.: Seceqp: A secure and efficient scheme for sknn query problem over encrypted geodata on cloud. In: 2019 IEEE 35th International Conference on Data Engineering (ICDE), pp. 662–673. IEEE (2019)

  30. Lei, X., Tu, G.-H., Liu, A.X., Xie, T.: Fast and secure k nn query processing in cloud computing. In: 2020 IEEE Conference on Communications and Network Security (CNS), pp. 1–9. IEEE (2020)

  31. Guan, Y., Lu, R., Zheng, Y., Shao, J., Wei, G.: Toward oblivious location-based k-nearest neighbor query in smart cities. IEEE Internet Things J 8(18), 14219–14231 (2021). https://doi.org/10.1109/JIOT.2021.3068859

    Article  Google Scholar 

  32. Samanthula, B. K., Elmehdwi, Y., Jiang, W.: K-nearest neighbor classification over semantically secure encrypted relational data. IEEE Trans. Knowledge Data Eng 27(5), 1261–1273 (2014)

    Article  Google Scholar 

  33. Wu, W., Liu, J., Rong, H., Wang, H., Xian, M.: Efficient k-nearest neighbor classification over semantically secure hybrid encrypted cloud database. IEEE Access 6, 41771–41784 (2018)

    Article  Google Scholar 

  34. Liu, L., Su, J., Liu, X., Chen, R., Huang, K., Deng, R. H., Wang, X.: Toward highly secure yet efficient k nn classification scheme on outsourced cloud data. IEEE Internet of Things J. 6(6), 9841–9852 (2019)

    Article  Google Scholar 

  35. Tan, Y., Wu, W., Liu, J., Wang, H., Xian, M.: Lightweight edge-based k nn privacy-preserving classification scheme in cloud computing circumstance. Concurrency Comput. Practice Exp. 32(19), 5804 (2020)

    Google Scholar 

  36. Kim, H.-J., Shin, J.-H., Chang, J.-W.: A secure and efficient k nn classification algorithm using encrypted index search and yao’s garbled circuit over encrypted databases. In: International Conference on Future Data and Security Engineering, pp. 21–38. Springer (2018)

  37. Oliveira, S. R., Zaiane, O. R.: Privacy preserving clustering by data transformation. J. Inf. Data Manag. 1(1), 37–37 (2010)

    Google Scholar 

  38. Sun, W., Wang, B., Cao, N., Li, M., Lou, W., Hou, Y. T., Li, H.: Privacy-preserving multi-keyword text search in the cloud supporting similarity-based ranking. In: Proceedings of the 8th ACM SIGSAC Symposium on Information, Computer and Communications Security, pp. 71–82 (2013)

  39. Paillier, P.: Public-key cryptosystems based on composite degree residuosity classes. In: International Conference on the Theory and Applications of Cryptographic Techniques, pp. 223–238. Springer (1999)

  40. Liu, Q., Hao, Z., Peng, Y., Jiang, H., Wu, J., Peng, T., Wang, G., Zhang, S.: Secvkq: Secure and verifiable k nn queries in sensor–cloud systems. J. Syst. Archit. 120, 102300 (2021)

    Article  Google Scholar 

  41. Yang, S., Tang, S., Zhang, X.: Privacy-preserving k nearest neighbor query with authentication on road networks. J. Parallel Distrib. Comput. 134, 25–36 (2019)

    Article  Google Scholar 

  42. Elgamal, T.: A public key cryptosystem and a signature scheme based on discrete logarithms. IEEE Trans. Inf. Theory 31(4), 469–472 (1985). https://doi.org/10.1109/TIT.1985.1057074

    Article  MathSciNet  MATH  Google Scholar 

  43. Cui, N., Yang, X., Wang, B., Geng, J., Li, J.: Secure range query over encrypted data in outsourced environments. World Wide Web 23(1), 491–517 (2020)

    Article  Google Scholar 

  44. Delfs, H., Knebl, H.: Introduction to Cryptography, 2nd edn. Springer, Berlin (2007)

    Book  MATH  Google Scholar 

  45. Liu, K., Giannella, C., Kargupta, H.: An attacker’s view of distance preserving maps for privacy preserving data mining. In: European Conference on Principles of Data Mining and Knowledge Discovery, pp 297–308. Springer (2006)

  46. LeCun, Y.: The mnist database of handwritten digits. http://yann.lecun.com/exdb/mnist/. Accessed 24 May 2021 (1998)

Download references

Funding

This work is supported by National Natural Science Foundation of China (61702294).

Author information

Authors and Affiliations

  1. College of Computer Science and Technology, Qingdao University, Qingdao, 266071, Shandong, China

    Kun Yang, Chengliang Tian & Hequn Xian

  2. College of Big Data and Internet, Shenzhen Technology University, Shenzhen, 518118, Guangdong, China

    Weizhong Tian

  3. College of Electromechanical Engineering, Qingdao University of Science and Technology, Qingdao, 266061, Shandong, China

    Yan Zhang

Authors
  1. Kun Yang
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Chengliang Tian
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Hequn Xian
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Weizhong Tian
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. Yan Zhang
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Chengliang Tian.

Ethics declarations

Conflict of Interests

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yang, K., Tian, C., Xian, H. et al. Query on the cloud: improved privacy-preserving k-nearest neighbor classification over the outsourced database. World Wide Web 26, 1747–1774 (2023). https://doi.org/10.1007/s11280-022-01093-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11280-022-01093-4

Keywords