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Encrypted Classification Using Secure K-Nearest Neighbour Computation

  • B. Praeep Kumar ReddyEmail author
  • Ayantika Chatterjee
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11947)

Abstract

Machine learning (ML) is one of the growing areas of engineering with sweeping applications. Executing machine learning algorithms on vast amount of data raises demand of huge resources and large data set handling. Thus, machine learning was too costly for many enterprise budgets. However, cloud service suppliers are making this technology reasonable to enterprises by offering massive shared resources. Machine learning as a service (MlaaS) is a category of cloud computing services that provides machine learning tools to allow customers to run, develop and manage applications in cloud without the complexity of building and maintaining. However, ascent of machine learning as a service procreates scenarios where one faces concealment dilemma, where the model must be revealed to the outsourced platform. Hence, cloud data security is an important issue where users can fancy the ability of executing applications by outsourcing sensitive data. Fully Homomorphic Encryption (FHE) offers a refined way to accommodate these conflicting interests in the cloud scenario by preserving data confidentiality as well as applying Mlaas in secure domain. However, processing on FHE data can not be directly performed on traditional instruction execution flow, but requires special circuit based representation of algorithms. In this paper, we focus on realizing K-Nearest Neighbour (KNN) computation on encrypted data, where data is stored using a generalized encrypted representation. Such representation will be suitable for easily extending to encrypted ensemble learning framework supporting multiple encrypted learners for higher accuracy. Extensive performance studies are carried out to evaluate the timing overhead of the encrypted KNN computation.

Keywords

Cloud FHE Machine learning KNN 

References

  1. 1.
    Park, H., Kim, P., Kim, H., Park, K.-W., Lee, Y.: Efficient machine learning over encrypted data with non-interactive communication. Comput. Stand. Interfaces 58, 87–108 (2017)CrossRefGoogle Scholar
  2. 2.
    Jäschke, A., Armknecht, F.: Unsupervised machine learning on encrypted. In: Cid, C., Jacobson Jr., M. (eds.) SAC 2018. LNCS. Springer, Cham (2018).  https://doi.org/10.1007/978-3-030-10970-7_21CrossRefGoogle Scholar
  3. 3.
    Bost, R., Popa, R.A., Goldwasser, S.: Machine learning classification over encrypted data. In: IACR Cryptology ePrint Archive 2014.  https://doi.org/10.14722/ndss.2015.23241
  4. 4.
    Hall, R., Fienberg, S.E., Nardi, Y.: Secure multiple linear regression based on homomorphic encryption (2011)Google Scholar
  5. 5.
    Park, H., Kim, P., Kim, H., Park, K.-W., Lee, Y.: Efficient machine learning over encrypted data with non-interactive communication. Comput. Stand. Interfaces 58, 87–108 (2018) CrossRefGoogle Scholar
  6. 6.
    Kesarwani, M., et al.: Efficient secure k-nearest neighbours over encrypted data. In: EDBT (2018).  https://doi.org/10.5441/002/edbt.2018.67
  7. 7.
    Yang, H., He, W., Li, J., Li, H.: Efficient and secure kNN classification over encrypted data using vector homomorphic encryption. In: 2018 IEEE International Conference on Communications (ICC), pp. 1–7 (2018) Google Scholar
  8. 8.
    Chen, H., et al.: Logistic regression over encrypted data from fully homomorphic encryption. BMC Med. Genomics 11, 81 (2018)CrossRefGoogle Scholar
  9. 9.
    Hu, S., Wang, Q., Wang, J., Chow, S.S.M., Zou, Q.: Securing fast learning! Ridge regression over encrypted big data. In: 2016 IEEE Trustcom/BigDataSE/ISPA, pp. 19–26 (2016)Google Scholar
  10. 10.
    Laur, S., Lipmaa, H., Mielikinen, T.: Cryptographically private support vector machines. In: 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2016)Google Scholar
  11. 11.
    Liu, F., Ng, W.K., Zhang, W.: Encrypted SVM for outsourced data mining. In: IEEE 8th International Conference on Cloud Computing (2015).  https://doi.org/10.1109/CLOUD.2015.158
  12. 12.
    Hesamifard, E., Takabi, H., Ghasemi, M.: CryptoDL: deep neural networks over encrypted data. CoRR abs/1711.05189 (2017)Google Scholar
  13. 13.
    Yao, B., Li, F., Xiao, X.: Secure nearest neighbor revisited. In: 2013 IEEE 29th International Conference on Data Engineering (ICDE), pp. 733–744 (2013)Google Scholar
  14. 14.
    Elmehdwi, Y., Samanthula, B.K., Jiang, W.: Secure k-nearest neighbor query over encrypted data in outsourced environments. In: IEEE 30th International Conference on Data Engineering, pp. 664–675 (2014)Google Scholar
  15. 15.
    Yi, X., Paulet, R., Bertino, E., Varadharajan, V.: Practical k nearest neighbor queries with location privacy. In: IEEE 30th International Conference on Data Engineering, pp. 640–651 (2014)Google Scholar
  16. 16.
    Wang, B., Hou, Y., Li, M.: Practical and secure nearest neighbor search on encrypted large-scale data. In: IEEE INFOCOM 2016 - The 35th Annual IEEE International Conference on Computer Communications, pp. 1–9 (2016)Google Scholar
  17. 17.
    Thosar, D.S., Thosar, R.D., Gadakh, P.J.: Secure kNN Query Processing in Untrusted Cloud Environments (2015)Google Scholar
  18. 18.
    Wong, W.K., Cheung, D.W.-L., Kao, B., Mamoulis, N.: Secure kNN computation on encrypted databases. In: SIGMOD Conference (2009)Google Scholar
  19. 19.
    Freedman, D.A.: Statistical Models: Theory and Practice. Cambridge University Press, Cambridge (2009)CrossRefGoogle Scholar
  20. 20.
    Rencher, A.C., Christensen, W.F.: Multivariate regression, Chap. 10 (2012)Google Scholar
  21. 21.
    Introduction. In: Methods of Multivariate Analysis. Wiley Series in Probability and Statistics, vol. 709, 3rd edn., p. 19. Wiley. ISBN 9781118391679 Google Scholar
  22. 22.
    Harrell, F.E.: Regression Modeling Strategies, 2nd edn. Springer, Cham (2001).  https://doi.org/10.1007/978-3-319-19425-7. ISBN 978-0-387-95232-1CrossRefzbMATHGoogle Scholar
  23. 23.
    Caruana, R., Niculescu-Mizil, A.: An empirical comparison of supervised learning algorithms. In: 23rd International Conference on Machine Learning. CiteSeerX (2006)Google Scholar
  24. 24.
    Pagel, J.F., Kirshtein, P.: Machine Dreaming and Consciousness (2017)Google Scholar
  25. 25.
    Songhori, E.M., Hussain, S.U., Sadeghi, A.-R., Koushanfar, F.: Compacting privacy-preserving k-nearest neighbor search using logic synthesis. In: 52nd ACM/EDAC/IEEE Design Automation Conference (DAC), pp. 1–6 (2015)Google Scholar
  26. 26.
    Zhu, Y., Huang, Z., Takagi, T.: Secure and controllable k-NN query over encrypted cloud data with key confidentiality. Parallel Distrib. Comput. 89, 1–12 (2016)CrossRefGoogle Scholar
  27. 27.
    Kaur, G., Pandey, P.S.: Emotion recognition system using IOT and machine learning-a healthcare application. In: 23rd Conference of Open Innovations Association FRUCT, p. 63. FRUCT Oy (2018)Google Scholar
  28. 28.
    Chatterjee, A., Sengupta, I.: Translating algorithms to handle fully homomorphic encrypted data on the cloud. IEEE Trans. Cloud Comput. 6, 287–300 (2018)CrossRefGoogle Scholar
  29. 29.
    Chatterjee, A., Sengupta, I.: Searching and sorting of fully homomorphic encrypted data on cloud. IACR Cryptology ePrint Archive 2015: 981 (2015)Google Scholar
  30. 30.
    Chillotti, I., Gama, N., Georgieva, M., Izabachène, M.: TFHE: fast fully homomorphic encryption over the torus. J. Cryptol. 1–58 (2018)Google Scholar
  31. 31.
    Carpov, S., Gama, N., Georgieva, M., Troncoso-Pastoriza, J.R.: Privacy-preserving semi-parallel logistic regression training with Fully Homomorphic Encryption. IACR Cryptology ePrint Archive 2019: 101 (2019)Google Scholar
  32. 32.
    Bourse, F., Minelli, M., Minihold, M., Paillier, P.: Fast homomorphic evaluation of deep discretized neural networks. In: Shacham, H., Boldyreva, A. (eds.) CRYPTO 2018, Part III. LNCS, vol. 10993, pp. 483–512. Springer, Cham (2018).  https://doi.org/10.1007/978-3-319-96878-0_17CrossRefGoogle Scholar
  33. 33.
    Dua, D., Graff, C.: UCI Machine Learning Repository (2017). http://archive.ics.uci.edu/ml
  34. 34.
    Gentry, C.: Computing arbitrary functions of encrypted data. Commun. ACM 53(3), 97–105 (2010)CrossRefGoogle Scholar
  35. 35.
  36. 36.
  37. 37.
  38. 38.
    Ultsch, A.: Clustering with SOM: U*C. In: Proceedings of Workshop on Self-Organizing Maps (2005)Google Scholar
  39. 39.
    Bishop, C.M.: Pattern Recognition and Machine Learning. Springer, New York (2006)zbMATHGoogle Scholar
  40. 40.
    Chillotti, I., Gama, N., Georgieva, M., Izabachène, M.: Faster packed homomorphic operations and efficient circuit bootstrapping for TFHE. In: Takagi, T., Peyrin, T. (eds.) ASIACRYPT 2017. LNCS, vol. 10624, pp. 377–408. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-70694-8_14CrossRefGoogle Scholar
  41. 41.
    Chillotti, I., Gama, N., Georgieva, M., Izabachène, M.: Faster fully homomorphic encryption: bootstrapping in less than 0.1 seconds. In: Cheon, J.H., Takagi, T. (eds.) ASIACRYPT 2016. LNCS, vol. 10031, pp. 3–33. Springer, Heidelberg (2016).  https://doi.org/10.1007/978-3-662-53887-6_1CrossRefzbMATHGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Indian Institute of Technology KharagpurKharagpurIndia

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