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Secure Naïve Bayesian Classification over Encrypted Data in Cloud

  • Xingxin Li
  • Youwen Zhu
  • Jian Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10005)

Abstract

To enjoy the advantage of cloud service while preserving security and privacy, huge data is increasingly outsourced to cloud in encrypted form. Unfortunately, encryption may impede the analysis and computation over the outsourced dataset. Naïve Bayesian classification is an effective algorithm to predict the class label of unlabeled samples. In this paper, we investigate naïve Bayesian classification on encrypted dataset in cloud and propose a secure scheme for the challenging problem. In our scheme, all the computation task of naïve Bayesian classification are completed by the cloud, which can dramatically reduce the burden of data owner and users. Based on the theoretical proof, our scheme can guarantee the security of both input dataset and output classification results, and the cloud can learn nothing useful about the training data of data owner and the test samples of users throughout the computation. Additionally, we evaluate our computation complexity and communication overheads in detail.

Keywords

Cloud security Naïve Bayesian classification Privacy 

Notes

Acknowledgements

We thank the anonymous reviewers and our shepherd, Prof. Xun Yi, for their valuable feedbacks. This work is partly supported by the Natural Science Foundation of Jiangsu Province of China (No. BK20150760), the Fundamental Research Funds for the Central Universities (No. NZ2015108, NS2016094), the China Postdoctoral Science Foundation funded project (No. 2015M571752), and the Natural Science Foundation of China (No. 61472470).

References

  1. 1.
    Bellazzi, R., Zupan, B.: Predictive data mining in clinical medicine: current issues and guidelines. Int. J. Med. Inform. 77(2), 81–97 (2008)CrossRefGoogle Scholar
  2. 2.
    Boneh, D., Goh, E.-J., Nissim, K.: Evaluating 2-DNF formulas on ciphertexts. In: Kilian, J. (ed.) TCC 2005. LNCS, vol. 3378, pp. 325–341. Springer, Heidelberg (2005). doi: 10.1007/978-3-540-30576-7_18 CrossRefGoogle Scholar
  3. 3.
    Bost, R., Popa, R.A., Tu, S., Goldwasser, S.: Machine learning classification over encrypted data. In: The Network and Distributed System Security Symposium (NDSS), pp. 1–14 (2015)Google Scholar
  4. 4.
    Clifton, C., Kantarcioglu, M., Vaidya, J., Lin, X., Zhu, M.Y.: Tools for privacy preserving distributed data mining. ACM Sigkdd Explorations Newslett. 4(2), 28–34 (2002)CrossRefGoogle Scholar
  5. 5.
    Clifton, C., Vaidya, J., Kantarcioglu, M.: Privacy-preserving naïve Bayes classification. VLDB J. 17(4), 879–898 (2008)CrossRefGoogle Scholar
  6. 6.
    Dong, C., Chen, L., Camenisch, J., Russello, G.: Fair private set intersection with a semi-trusted arbiter. In: Wang, L., Shafiq, B. (eds.) DBSec 2013. LNCS, vol. 7964, pp. 128–144. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  7. 7.
    Elgamal, T.: A public key cryptosystem and a signature scheme based on discrete logarithms. IEEE Trans. Inf. Theory 31(4), 469–472 (1985)MathSciNetCrossRefzbMATHGoogle Scholar
  8. 8.
    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 (ICDE), pp. 664–675 (2014)Google Scholar
  9. 9.
    Goldreich, O.: Foundations of Cryptography: Volume II, Basic Applications. Cambridge University Press, Cambridge (2004)CrossRefzbMATHGoogle Scholar
  10. 10.
    Kantarcıoglu, M., Vaidya, J., Clifton, C.: Privacy preserving naive Bayes classifier for horizontally partitioned data. In: IEEE ICDM workshop on privacy preserving data mining, pp. 3–9 (2003)Google Scholar
  11. 11.
    Kim, H.J., Kim, J.U., Ra, Y.G.: Boosting naïve Bayes text classification using uncertainty-based selective sampling. Neurocomputing 67, 403–410 (2005)CrossRefGoogle Scholar
  12. 12.
    Lindell, Y., Pinkas, B.: Privacy preserving data mining. J. Cryptology 15(3), 36–54 (2002)MathSciNetCrossRefzbMATHGoogle Scholar
  13. 13.
    Liu, A., Zhengy, K., Liz, L., Liu, G., Zhao, L., Zhou, X.: Efficient secure similarity computation on encrypted trajectory data. In: IEEE 31st International Conference on Data Engineering (ICDE), pp. 66–77 (2015)Google Scholar
  14. 14.
    Liu, X., Lu, R., Ma, J., Chen, L., Qin, B.: Privacy-preserving patient-centric clinical decision support system on naive Bayesian classification. IEEE J. Biomed. Health Inform. 20(2), 655–668 (2016)CrossRefGoogle Scholar
  15. 15.
    Lops, P., Gemmis, M.D., Semeraro, G.: Content-based recommender systems: state of the art and trends. In: Recommender Systems Handbook, pp. 73–105 (2011)Google Scholar
  16. 16.
    Mitchell, T.: Machine Learning, 1st edn. McGraw-Hill Science/Engineering/Math, New York (1997)zbMATHGoogle Scholar
  17. 17.
    Paillier, P.: Public-key cryptosystems based on composite degree residuosity classes. In: Stern, J. (ed.) EUROCRYPT 1999. LNCS, vol. 1592, pp. 223–238. Springer, Heidelberg (1999)Google Scholar
  18. 18.
    Samanthula, B.K., Elmehdwi, Y., Jiang, W.: k-nearest neighbor classification over semantically secure encrypted relational data. IEEE Trans. Knowl. Data Eng. 27(5), 1261–1273 (2015)CrossRefGoogle Scholar
  19. 19.
    Samanthula, B.K., Jiang, W.: Efficient privacy-preserving range queries over encrypted data in cloud computing. In: IEEE Sixth International Conference on Cloud Computing, pp. 51–58 (2013)Google Scholar
  20. 20.
    Yang, Z., Zhong, S., Wright, R.N.: Privacy-preserving classification of customer data without loss of accuracy. In: Siam International Conference on Data Mining, pp. 92–102 (2005)Google Scholar
  21. 21.
    Yao, A.: How to generate and exchange secrets. In: 27th Annual Symposium on Foundations of Computer Science, pp. 162–167. IEEE (1986)Google Scholar
  22. 22.
    Yi, X., Zhang, Y.: Privacy-preserving naive Bayes classification on distributed data via semi-trusted mixers. Inform. Syst. 34(3), 371–380 (2009)CrossRefGoogle Scholar
  23. 23.
    Yuan, J., Yu, S.: Privacy preserving back-propagation neural network learning made practical with cloud computing. IEEE Trans. Parallel Distrib. Syst. 25(1), 212–221 (2014)CrossRefGoogle Scholar
  24. 24.
    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)CrossRefGoogle Scholar
  25. 25.
    Zhu, Y., Wang, Z., Zhang, Y.: Secure k-NN query on encrypted cloud data with limited key-disclosure and offline data owner. In: The 20th Pacific-Asia Conference on Knowledge Discovery and Data Mining, pp. 401–414 (2016)Google Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.College of Computer Science and TechnologyNanjing University of Aeronautics and AstronauticsNanjingChina

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