Privacy Preservation in Distributed Environment Using RSA-CRT

  • Raghvendra Kumar
  • Prasant Kumar Pattnaik
  • Yogesh Sharma
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 379)


Most data mining applications are based on information sharing and additional challenges, when we are dealing with data that are containing sensitive or private information. There is no common data mining technique available that deals with private information without any leakage. Therefore, the knowledge extracted from such data may disclose the pattern with sensitive/private information. This may put privacy on the individual/group of parties. In the past few years, privacy preserving data mining has attracted the research interest and potential for a wide area of applications. There are many techniques for privacy preservation like cryptography, anonymity, and randomization, etc., experimented for privacy preservation in data mining. In this paper, information system-based approach is considered, so some of the attributes required higher privacy compared to the other attributes. This paper explores the use of cryptography techniques, namely RSA with Chinese remainder theorem (CRT) to encrypt and decrypt the database, when all the parties are trying to find their global result in presence of trusted third party or absence of trusted third party, without disclosing their private information to each other.


  1. 1.
    Apostol, T.: Introduction to Analytical Number Theory, Student edn. Springer International (1989)Google Scholar
  2. 2.
    Boneh, D., Shacham, H.: Fast variants of RSA. Crypto Bytes, Springer 5(1), 1–9 (2002)Google Scholar
  3. 3.
    Sun, H.M., Wu, M.E.: An approach towards rebalanced RSA-CRT with short public exponent. Cryptology ePrint Archive: Report 2005/053 (2005)Google Scholar
  4. 4.
    Agrawal, S., Krishnan, V., Haritsa, J.R.: On addressing efficiency concerns in privacy-preserving mining. In: Proceedings of the 9th International Conference on Database Systems for Advanced Applications. LNCS 2973, pp. 113–124. Springer-Verlag, Jeju IslandGoogle Scholar
  5. 5.
    Peng, Z., Yun-Hai, T., Shi-Wei, T., Dong-Qing, Y., Xiu-Li, M.: An effective method for privacy preserving association rule mining. J. Softw. 17(8), 1764–1773 (2006)CrossRefGoogle Scholar
  6. 6.
    Linedell, Y., Pinkas, B.: Secure multiparty computation for privacy-preserving data mining. J. Privacy Confidentiality 1(1) 59–98 ((2009))Google Scholar
  7. 7.
    Paillier, P.: Public-key cryptosystems based on composite degree residuosity classes. Adv. Cryptog. EUROCRYPT ’99, pp. 223–238. Prague, Czech Republic (1999)Google Scholar
  8. 8.
    Rivest, R., Adleman, L., Dertouzos, M.: On data banks and privacy homomorphism’s. In: R.A. De Milloetal (ed.) Foundations of Secure Computation, pp. 169–179. Academic Press (2000)Google Scholar
  9. 9.
    Qiong, G., Xiao-hui, C.: A privacy preserving distributed for mining association rules. Int. Conf. Artif. Intell. Comput. Intell. 294–297 (2009)Google Scholar
  10. 10.
    Koblitz, N.: A Course in Number Theory and Cryptography, 2nd edn. Springer (1994)Google Scholar
  11. 11.
    Stallings, W.: Cryptography and network security, 3rd edn. Pearson EducationGoogle Scholar
  12. 12.
    Yao, A.C.: Protocol for secure computations. In: Proceedings of the 23rd Annual IEEE Symposium on Foundation of Computer Science, pp. 160–164 (1982)Google Scholar
  13. 13.
    Gold Reich, O., Micali, S., Wigderson, A.: How to play any mental game. In: Proceedings of the 19th Annual ACM Symposium on Theory of Computation, pp. 218–229 (1987)Google Scholar

Copyright information

© Springer India 2016

Authors and Affiliations

  • Raghvendra Kumar
    • 1
  • Prasant Kumar Pattnaik
    • 2
  • Yogesh Sharma
    • 1
  1. 1.Faculty of Engineering and TechnologyJodhpur National UniversityJodhpurIndia
  2. 2.School of Computer EngineeringKIIT UniversityBhubaneswarIndia

Personalised recommendations