A Privacy-Preserving Classifier in Statistic Pattern Recognition

  • Qi Wang
  • Dehua ZhouEmail author
  • Quanlong Guan
  • Yanling Li
  • Jimian Yang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11064)


Machine learning classification and pattern recognition are widely used in various scenarios nowadays, such as medical diagnosis and face recognition, both need to estimate the similarity measure between different samples. In such applications, it is critical to protect the privacy of both the private input data and the machine learning model. In this paper, we propose a privacy-preserving Mahalanobis distance scheme for the statistic pattern recognition, and construct a protocol for the privacy-preserving prediction phase by using the labeled-homomorphic encryption scheme, which combines linearly homomorphic encryption and pseudo-random function. And we consider an outsouring scenario, which most work to be outsourced to the cloud server. Our design goal is to ensure that the client’s private data is permanently confidential and protect the secret model in cloud server. Most of the previous work proposed complex schemes with too many interactions between the clients and cloud server, and we propose an efficient scheme to minimal the complexity of the client side.


Classification Homomorphic encryption Cloud computing 



This work is supported by NSFC (61602210), Science and Technology Project of Guangzhou City (No. 201707010320), Natural Science Foundation of Guangdong Province (No. 2014A030310156), the Fundamental Research Funds for the Central Universities (21617408), the Science and Technology Planning Project of Guangdong Province, China (2014A040401027, 2015A030401043, 2017A040405029).


  1. 1.
    Shah, V.S., Shah, H.R., Samui, P., Ramachandra Murthy, A.: Prediction of fracture parameters of high strength and ultra-high strength concrete beams using minimax probability machine regression and extreme learning machine. Comput. Mater. Continua 44(2), 73–84 (2014)Google Scholar
  2. 2.
    Jayaprakash, G., Muthuraj, M.P.: Prediction of compressive strength of various SCC mixes using relevance vector machine. Comput. Mater. Continua 54(1), 83–102 (2015)Google Scholar
  3. 3.
    Rahman, F., Addo, I.D., Ahamed, S.I., Yang, J.J., Wang, Q.: Privacy challenges and goals in mHealth systems. In: Advances in Computers (2016)Google Scholar
  4. 4.
    Yao, A.C.: Protocols for secure computations. In: Proceedings of the IEEE Symposium on Foundations of Computer Science, pp. 160–164 (1982)Google Scholar
  5. 5.
    Goldreich, O., Micali, S., Wigderson, A.: How to play any mental game. In: Nineteenth ACM Symposium on Theory of Computing, pp. 218–229 (1987)Google Scholar
  6. 6.
    Goldwasser, S., Micali, S.: Probabilistic encryption. J. Comput. Syst. Sci. 28(2), 270–299 (1984)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Paillier, P.: Public-key cryptosystems based on composite degree residuosity classes. In: International Conference on Theory and Application of Cryptographic Techniques, pp. 223–238 (1999)Google Scholar
  8. 8.
    Lindell, Y., Pinkas, B.: Privacy preserving data mining. Adv. Cryptol. 15(3), 177–206 (2000)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Gascn, A., et al.: Privacy-preserving distributed linear regression on high-dimensional data, vol. 2017, no. 4, pp. 345–364 (2017)Google Scholar
  10. 10.
    Nikolaenko, V., Weinsberg, U., Ioannidis, S., Joye, M., Dan, B., Taft, N.: Privacy-preserving ridge regression on hundreds of millions of records. In: Security and Privacy, pp. 334–348 (2013)Google Scholar
  11. 11.
    Aono, Y., Hayashi, T., Trieu Phong, L., Wang, L.: Scalable and secure logistic regression via homomorphic encryption, vol. 22, no. 1, pp. 142–144 (2016)Google Scholar
  12. 12.
    Wu, D.J., Feng, T., Naehrig, M., Lauter, K.: Privately evaluating decision trees and random forests. In: Proceedings on Privacy Enhancing Technologies 2016, no. 4 (2016)Google Scholar
  13. 13.
    Shokri, R., Shmatikov, V.: Privacy-preserving deep learning. In: ACM SIGSAC Conference on Computer and Communications Security, pp. 1310–1321 (2015)Google Scholar
  14. 14.
    Graepel, T., Lauter, K., Naehrig, M.: ML confidential: machine learning on encrypted data. In: Kwon, T., Lee, M.-K., Kwon, D. (eds.) ICISC 2012. LNCS, vol. 7839, pp. 1–21. Springer, Heidelberg (2013). Scholar
  15. 15.
    Bos, J.W., Lauter, K., Naehrig, M.: Private predictive analysis on encrypted medical data. J. Biomed. Inf. 50(8), 234–243 (2014)CrossRefGoogle Scholar
  16. 16.
    De Maesschalck, R., Jouan-Rimbaud, D., Massart, D.L.: The mahalanobis distance. Chemom. Intell. Lab. Syst. 50(1), 1–18 (2000)CrossRefGoogle Scholar
  17. 17.
    Gentry, C.: Fully homomorphic encryption using ideal lattices. In: ACM Symposium on Theory of Computing. STOC 2009, 31 May–June, Bethesda, MD, USA, pp. 169–178 (2009)Google Scholar
  18. 18.
    Brakerski, Z., Gentry, C., Vaikuntanathan, V.: (Leveled) fully homomorphic encryption without bootstrapping. In: Innovations in Theoretical Computer Science Conference, pp. 309–325 (2012)Google Scholar
  19. 19.
    Catalano, D., Fiore, D.: Using linearly-homomorphic encryption to evaluate degree-2 functions on encrypted data. In: ACM SIGSAC Conference on Computer and Communications Security, pp. 1518–1529 (2015)Google Scholar
  20. 20.
    Barbosa, M., Catalano, D., Fiore, D.: Labeled homomorphic encryption. In: Foley, S.N., Gollmann, D., Snekkenes, E. (eds.) ESORICS 2017. LNCS, vol. 10492, pp. 146–166. Springer, Cham (2017). Scholar
  21. 21.
    Zhu, Y., Huang, L., Yang, W., Li, D., Luo, Y., Dong, F.: Three new approaches to privacy-preserving add to multiply protocol and its application. In: Second International Workshop on Knowledge Discovery and Data Mining, pp. 554–558 (2009)Google Scholar
  22. 22.
    Li, Y., Jiang, Z.L., Wang, X., Yiu, S.M.: Privacy-preserving ID3 data mining over encrypted data in outsourced environments with multiple keys. In: IEEE International Conference on Computational Science and Engineering, pp. 548–555 (2017)Google Scholar
  23. 23.
    Mohassel, P., Zhang, Y.: SecureML: a system for scalable privacy-preserving machine learning. In: Security and Privacy, pp. 19–38 (2017)Google Scholar
  24. 24.
    Stan, O., Zayani, M.-H., Sirdey, R., Hamida, A.B., Leite, A.F., Mziou-Sallami, M.: A new crypto-classifier service for energy efficiency in smart cities. Cryptology ePrint Archive, Report 2017/1212 (2017).
  25. 25.
    Bost, R., Popa, R.A., Tu, S., Goldwasser, S.: Machine learning classification over encrypted data. In: Network and Distributed System Security Symposium (2015)Google Scholar
  26. 26.
    Veugen, T.: Comparing encrypted data (2011)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Qi Wang
    • 1
  • Dehua Zhou
    • 1
    Email author
  • Quanlong Guan
    • 2
  • Yanling Li
    • 1
  • Jimian Yang
    • 1
  1. 1.Department of Computer ScienceJinan UniversityGuangzhouChina
  2. 2.Network and Educational Technology CenterJinan UniversityGuangzhouChina

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