Advertisement

Privacy-Preserving Naive Bayes Classification Using Fully Homomorphic Encryption

  • Sangwook Kim
  • Masahiro Omori
  • Takuya Hayashi
  • Toshiaki Omori
  • Lihua Wang
  • Seiichi Ozawa
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11304)

Abstract

Many services for data analysis require customer’s data to be exposed and privacy issues are critical in related fields. To address this problem, we propose a Privacy-Preserving Naive Bayes classifier (PP-NBC) model which provides classification results without leaking privacy information in data sources. Through classification process in PP-NBC, the operations are evaluated using encrypted data by applying fully homomorphic encryption scheme so that service providers are able to handle customer’s data without knowing their actual values. The proposed method is implemented with a homomorphic encryption library called HElib and we carry out a primitive performance evaluation for the proposed PP-NBC.

Keywords

Privacy-preserving data mining Machine learning Naive Bayes Fully homomorphic encryption Classification 

References

  1. 1.
    Amazon Web Services (AWS). https://aws.amazon.com/
  2. 2.
  3. 3.
    EU GDPR Information Portal. http://eugdpr.org/eugdpr.org-1.html
  4. 4.
    Konen, J., McMahan, H.B., Yu, F.X., Richtarik, P., Suresh, A.T., Bacon, D.: Federated learning: strategies for improving communication efficiency. In: NIPS Workshop on Private Multi-Party Machine Learning (2016)Google Scholar
  5. 5.
    Li, X., Zhu, Y., Wang, J.: Secure Naïve Bayesian classification over encrypted data in cloud. In: Chen, L., Han, J. (eds.) ProvSec 2016. LNCS, vol. 10005, pp. 130–150. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-47422-9_8CrossRefGoogle Scholar
  6. 6.
    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).  https://doi.org/10.1007/3-540-48910-X_16CrossRefGoogle Scholar
  7. 7.
    Gentry, C.: A Fully Homomorphic Encryption Scheme. Dissertation for Ph.D. degree, Stanford University, United States - California (2009)Google Scholar
  8. 8.
    Fisher, R.A.: The use of multiple measurements in taxonomic problems. Ann. Eugenics 7(2), 179–188 (1936)CrossRefGoogle Scholar
  9. 9.
    UCI Machine Learning Repository. http://archive.ics.uci.edu/ml
  10. 10.
    Dean, J., Ghemawat, S.: MapReduce: simplified data processing on large clusters. Commun. ACM 51, 107–113 (2008)CrossRefGoogle Scholar
  11. 11.
    Yao, A.C.: How to generate and exchange secrets. In: 27th Annual Symposium on Foundations of Computer Science, pp. 162–167 (1986)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Sangwook Kim
    • 1
  • Masahiro Omori
    • 1
  • Takuya Hayashi
    • 2
  • Toshiaki Omori
    • 1
  • Lihua Wang
    • 1
    • 2
  • Seiichi Ozawa
    • 1
  1. 1.Kobe UniversityKobeJapan
  2. 2.National Institute of Information and Communications TechnologyKoganeiJapan

Personalised recommendations