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Secure kNN query of outsourced spatial data using two-cloud architecture

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Abstract

Cloud service providers can offer virtual servers, with powerful processors and massive storage capacity for nominal per-use charges. With the underlying resources, the cloud allows data owners to trade capital expense for the variable cost. The auto-scaling features of the cloud can control the consumption of computing resources and hence the associated cost. However, by saving data on the cloud, the privacy of data becomes at risk. Data becomes exposed to the untrusted cloud. The users’ queries can also be monitored by attackers and cloud operators. Furthermore, the pattern of accessing data can be identified. The solution is to encrypt data before outsourcing to the cloud. However, answering queries over encrypted data becomes a challenge. This paper proposes a novel technique, called Secure Voronoi kNN (SVK), that answers k-nearest neighbor (kNN) queries over encrypted data in a two-cloud architecture. SVK can hide data access patterns from the cloud.

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Data availability

The spatial dataset used to test the proposed algorithms is a real-world dataset that can be publicly accessed through https://www.openstreetmap.org. The dataset represents spatial data points of the Road Network of Texas. The domain of the dataset was normalized to the unit square [0,1].

References

  1. Liu Z, Choo KKR, Zhao M (2017) Practical-oriented protocols for privacy-preserving outsourced big data analysis: challenges and future research directions. Comput Secur. https://doi.org/10.1016/j.cose.2016.12.006

    Article  Google Scholar 

  2. Hashim A (2020) Sina Weibo suffered data breach exposing 538 million records now on sale. https://latesthackingnews.com/2020/03/24/sina-weibo-suffered-data-breach-exposing-538-million-records-now-on-sale/#:~:text=SinaWeiboDataBreachReportedly%2CChinesesiteSina,Shu%29%2CCTOMoresec%2CpostedaboutitonWeibo. Accessed 13 Feb 2021

  3. CBSNewYork (2020) Data breach. CBSNewYork

  4. Bohli JM, Gruschka N, Jensen M et al (2013) Security and privacy-enhancing multicloud architectures. IEEE Trans Dependable Secure Comput 10:212–224. https://doi.org/10.1109/TDSC.2013.6

    Article  Google Scholar 

  5. Schoenmakers B (2011) Homomorphic encryption. In: van Tilborg HCA, Jajodia S (eds) Encyclopedia of cryptography and security. Springer, Boston

    Google Scholar 

  6. Paillier P (2011) Paillier encryption and signature schemes. In: van Tilborg HCA, Jajodia S (eds) Encyclopedia of cryptography and security. Springer, Boston

    Google Scholar 

  7. Hong J, Wen T, Guo Q, Ye Z (2017) Secure kNN computation and integrity assurance of data outsourcing in the cloud. Math Probl Eng. https://doi.org/10.1155/2017/8109730

    Article  Google Scholar 

  8. White DA, Jain R (1996) Similarity indexing with the SS-tree. In: Proceedings—International Conference on Data Engineering, pp 516–523. https://doi.org/10.1109/icde.1996.492202

  9. Delfs H, Knebl H (2015) Introduction to cryptography principles and applications, 3rd edn. Springer, Berlin

    Book  MATH  Google Scholar 

  10. Goethals B, Laur S, Lipmaa H, Mielik T (2004) On private scalar product computation for privacy-preserving data mining Bart. In: International Conference on Information Security and Cryptology, vol 3506, pp 104–120

  11. Oliveira SRM, Zaïane OR (2003) Privacy preserving clustering by data transformation. In: Proceedings of the 18th Brazilian Symposium on Databases, vol 1, pp 304–318. 10.1.1.2.42

  12. Zhu X, Wu J, Chang W, et al (2019) Authentication of multi-dimensional top-K query on untrusted server. In: IEEE/ACM 26th International Symposium on Quality of Service, IWQoS, pp 1–6. https://doi.org/10.1109/IWQoS.2018.8624145

  13. Yang G, Cai Y, Hu Z (2016) Authentication of function queries. In: IEEE 32nd International Conference on Data Engineering, ICDE 2016, pp 337–348. https://doi.org/10.1109/ICDE.2016.7498252

  14. Liu AX, Li R (2021) K-nearest neighbor queries over encrypted data. Algorithms for data and computation privacy. Springer, pp 79–108. https://doi.org/10.1007/978-3-030-58896-0

  15. Bloom BH (1970) Space/time trade-offs in hash coding with allowable errors. Commun ACM 13:422–426. https://doi.org/10.1145/362686.362692

    Article  MATH  Google Scholar 

  16. Wong WK, Cheung DW, Kao B, Mamoulis N (2009) Secure kNN encrypted databases. In: Proceedings of the 2009 ACM SIGMOD International Conference on Management of Data, pp 139–152

  17. Hu H, Xu J, Ren C, Choi B (2011) Processing private queries over untrusted data cloud through privacy homomorphism. In: Proceedings—International Conference on Data Engineering, pp 601–612. https://doi.org/10.1109/ICDE.2011.5767862

  18. Yi X, Paulet R, Bertino E, Varadharajan V (2014) Practical k nearest neighbor queries with location privacy. In: Proceedings—International Conference on Data Engineering, pp 640–651. https://doi.org/10.1109/ICDE.2014.6816688

  19. Wang B, Hou Y, Li M (2016) Practical and secure nearest neighbor search on encrypted large-scale data. In: Proceedings—IEEE Conference on Computer Communications, INFOCOM

  20. Yao B, Li F, Xiao X (2013) Secure nearest neighbor revisited. In: Proceedings—International Conference on Data Engineering, pp 733–744. https://doi.org/10.1109/ICDE.2013.6544870

  21. Demsetz H, Alchian A (2020) One-time, oblivious, and unlinkable query processing over encrypted data on cloud. Inf Commun Secur 62:350–365

    MathSciNet  Google Scholar 

  22. Gaikwad VS, Walse KH, Thakare VM (2022) Privacy preserving outsourced k nearest neighbors classification: comprehensive study. Springer, Singapore

    Google Scholar 

  23. Sun F, Yu J, Ge X et al (2021) Constrained top-k nearest fuzzy keyword queries on encrypted graph in road network. Comput Secur 111:102456. https://doi.org/10.1016/j.cose.2021.102456

    Article  Google Scholar 

  24. Yang Y, Miao Y, Choo KKR, Deng RH (2022) Lightweight privacy-preserving spatial keyword query over encrypted cloud data. In: Proceedings—International Conference on Distributed Computing Systems 2022-July, pp 392–402. https://doi.org/10.1109/ICDCS54860.2022.00045

  25. Song Z, Ren Y, He G (2022) Privacy-preserving KNN classification algorithm for smart grid. Secur Commun Netw. https://doi.org/10.1155/2022/7333175

    Article  Google Scholar 

  26. Oded G (2004) Foundations of cryptography: basic applications, vol 2. Cambridge University Press, Cambridge

    MATH  Google Scholar 

  27. Mohassel P, Zhang Y (2017) SecureML: a system for scalable privacy-preserving machine learning. In: Proceedings—IEEE Symposium on Security and Privacy, pp 19–38. https://doi.org/10.1109/SP.2017.12

  28. Data61 C (2018) Python Paillier documentation

  29. Openstreetmap. https://www.openstreetmap.org

  30. Paillier P (1999) Public-key cryptosystems based on composite degree residuosity classes. In: Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol 1592, pp 223–238. https://doi.org/10.1007/3-540-48910-X_16

  31. Cai C, Awano H, Ikeda M (2019) High-speed ASIC implementation of Paillier cryptosystem with homomorphism. In: IEEE 13th International Conference on ASIC, pp 1–4

  32. Jost C, Lam H, Maximov A, Smeets B (2015) Encryption performance improvements of the Paillier cryptosystem. IACR Cryptol Int Assoc Cryptol Res 8:864

    Google Scholar 

  33. Harerimana R, Tan SY, Yau WC (2017) A Java implementation of Paillier homomorphic encryption scheme. In: 2017 5th International Conference on Information and Communication Technology, ICoIC7 2017 0. https://doi.org/10.1109/ICoICT.2017.8074646

  34. Xue K, Li S, Hong J et al (2017) Two-cloud secure database for numeric-related SQL range queries with privacy preserving. IEEE Trans Inf Forensics Secur 12:1596–1608. https://doi.org/10.1109/TIFS.2017.2675864

    Article  Google Scholar 

  35. Hsu YC, Hsueh CH, Wu JL (2020) A privacy preserving cloud-based K-NN search scheme with lightweight user loads. Computers 9:1–27. https://doi.org/10.3390/computers9010001

    Article  Google Scholar 

  36. Li M, Zhang M, Gao J et al (2022) Repetitive, oblivious, and unlinkable SkNN over encrypted-and-updated data on cloud. Inf Commun Secur 1:261–280. https://doi.org/10.1007/978-3-031-15777-6

    Article  Google Scholar 

  37. D’Arco P, De Prisco R (2016) Secure computation without computers. Theor Comput Sci 651:11–36. https://doi.org/10.1016/j.tcs.2016.08.003

    Article  MathSciNet  MATH  Google Scholar 

  38. Zhu Y, Zhang Y, Yuan J, Wang X (2018) FTP: an approximate fast privacy-preserving equality test protocol for authentication in Internet of Things. Secur Commun Netw 2018:1–9. https://doi.org/10.1155/2018/6909703

    Article  Google Scholar 

  39. Biryukov A (2011) Chosen plaintext and chosen ciphertext attack. In: van Tilborg HCA, Jajodia S (eds) Encyclopedia of cryptography and security. Springer, Boston, p 205

    Google Scholar 

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Author notes

  1. Ibrahim Kamel and Zaher Al Aghbari have contributed equally to this work.

Authors and Affiliations

  1. Department of Computer Engineering, University of Sharjah, Sharjah, United Arab Emirates

    Tasneem Ghunaim & Ibrahim Kamel

  2. Department of Computer Science, University of Sharjah, Sharjah, United Arab Emirates

    Zaher Al Aghbari

Authors
  1. Tasneem Ghunaim
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  2. Ibrahim Kamel
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  3. Zaher Al Aghbari
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Contributions

Tasneem Ghunaim, Ibrahim Kamel, and Zaher Al Aghbari conceived and designed the proposed algorithms. Tasneem Ghunaim wrote the main manuscript text, implemented the proposed algorithms, executed the performance analysis, and concluded the results. Ibrahim Kamel and Zaher Al Aghbari outlined and modeled the different versions of the proposed protocols and studied the final results. All authors reviewed the manuscript.

Corresponding author

Correspondence to Tasneem Ghunaim.

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Ghunaim, T., Kamel, I. & Al Aghbari, Z. Secure kNN query of outsourced spatial data using two-cloud architecture. J Supercomput 79, 21310–21345 (2023). https://doi.org/10.1007/s11227-023-05495-7

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