Association rules and deep learning for cryptographic algorithm in privacy preserving data mining

  • N. Rajesh
  • A. Arul Lawrence Selvakumar


Security in database is important as it contains sensitized information. Data mining provides mechanisms for intermediate representation of data. Privacy preservation is vital due to the security aspects. Privacy denotes mechanisms for allowing the data to be accessed in secured manner. The basic idea is to protect the data that are sensitive from data miners such that it is not possible to pull out the sensitive data from database. Association rules are crucial idea to change the dataset from the original data set. Association rules with cryptographic techniques has been used. They also demonstrate the applicability by applying this algorithm on real life data sets. This research work proposed a well-organized privacy preservation data-mining scheme with data-mining perturbation merged approach. It uses the association rules with cryptography techniques. The paper also demonstrates how neural networks is being applied for predicting the medical dataset. The paper also provides scope on how deep convolution neural network can be applied for medical analysis.


Cryptography Decryption Encryption Association Perturbation Deep learning 


  1. 1.
    Sharma, S. Agrawal, J., Agarwal, S., Sharma, S.: Machine learning techniques for data mining: a survey. In: Proceeding of the IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), pp. 1–10 (2013)Google Scholar
  2. 2.
    Shyu, F.-M., Cheng, P.-H., Su, M.-J., Luh, J.-J., Chen, H.-S., Chen, S.-J.: Context-based model for mobile electronic medical records. In: Proceedings of the IEEE Conference, pp. 1–8 (2006)Google Scholar
  3. 3.
    McLatchey, J. , Barnett, G.O., McDonnell, G., Piggins, J., Zielstorff, R.D., Weidman-Dahl, F., Hoffer, E., Hupp, J.A.: The capturing of more detailed medical information in costar. In: Proceedings of the IEEE Conference, pp. 1–8 (1983)Google Scholar
  4. 4.
    Pisanelli, D.M., Ricci, F.L.: Electronic medical records: the aggregation of single events for health care planning and quality assurance. In: Proceedings of the IEEE Conference, pp. 1–8 (1991)Google Scholar
  5. 5.
    Shokri, R., Shmatikov, V.: Privacy-preserving deep learning. In: Proceedings of the Fifty-third Annual Allerton Conference, pp. 909–910 (2015)Google Scholar
  6. 6.
    Tahamtan, A., Eder, J.: Privacy preservation through process views. In: Proceedings of the 24th International Conference on Advanced Information Networking and Applications Workshops, pp. 1100–1107 (2010)Google Scholar
  7. 7.
    Meng, F., Liu, B., Wang, C.: Privacy preserving clustering over distributed data. In: Proceedings of the 3rd International Conference on Advanced Computer Theory and Engineering (ICACTE), pp. 544–548 (2010)Google Scholar
  8. 8.
    Nguyen, H.X., Roughan,M.: Multi-observer privacy-preserving hidden Markov models. In: Proceedings of the IEEE Network Operations and Management Symposium (NOMS): Short Paper, pp. 514–517 (2012)Google Scholar
  9. 9.
    Mendes, R., Vilela, J.P.: Privacy-preserving data mining: methods, metrics, and applications. IEEE Translat. Content Min. 5, 10562–10582 (2017)Google Scholar
  10. 10.
    Yu, C.-M., Chen, C.-Y., Chao, H.-C.: Privacy-preserving multikeyword similarity search over outsourced cloud data. IEEE Syst. J. 11(2), 385–393 (2017)CrossRefGoogle Scholar
  11. 11.
    Fung, B.C.M., Wang, K., Fu, A.W.-C., Yu, P.S.: Privacy-preserving data publishing: a survey of recent developments. In: Book—Introduction to Privacy-Preserving Data Publishing: Concepts and Techniques (2010)Google Scholar
  12. 12.
    Rajesh, N., Selvakumar, A.L.: Hiding personalised anonymity of attributes using privacy preserving data mining. Int. J. Adv. Intell. Paradig. 7(3/4), 395–396 (2015)CrossRefGoogle Scholar
  13. 13.
    Mahendran, M.: An efficient algorithm for privacy preserving data mining using heuristic approach. Int. J. Adv. Res. Comput. Commun. Eng. 1(9), 742–743 (2012)Google Scholar
  14. 14.
    Thakur, D., Gupta, H.: An exemplary study of privacy preserving association rule mining techniques. Int. J. Adv. Res. Comput. Sci. Softw. Eng. 3(11), 893–900 (2013)Google Scholar
  15. 15.
    Narang, G., Shaikh, A.: Preservation of privacy in mining using association rule technique. Int. J. Sci. Technol. Res. 2(3), 219–220 (2013)Google Scholar
  16. 16.
    Verma, D., Jha, D.K.: Association rules hiding algorithm for privacy preserving data mining. Int. J. Comput. Sci. Emerg. Trends (IJCSET) 02(01), 16–20 (2013)Google Scholar
  17. 17.
    Chintada, S.K., Madina, J.R.: A privacy preserving association rule mining over unrealized datasets. Int. J. Eng. Trends Technol. (IJETT) 5(4), 207–210 (2013)Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Research & Development CentreBharathiar UniversityCoimbatoreIndia
  2. 2.Department of Computer Science & EngineeringRajiv Gandhi Institute of TechnologyBangaloreIndia

Personalised recommendations