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A Survey on Privacy Preserving Data Mining Techniques


  • Computer Science and Engineering, SRM University, Chennai - 603203, Tamil Nadu, India


Objectives: Recently Privacy preserving data mining (PPDM) is known to be the most important aspect among researchers. As Privacy preserving data mining permits, sharing and exchanging of privacy susceptible data for analysis, it has grown more and more popular. Since one of the important aspects of data mining is safeguarding privacy, this paper aims to analyze different technique adopted for preserving privacy while maintaining the real characteristic of data under consideration. Methods/Statistical Analysis: In this paper, the authors evaluate the usefulness of PPDM techniques based on its performance, data usage, and uncertainty level and so on. The findings of authors and limitations in each technique are consolidated. Findings: Each technique has its unique way of usefulness apart from its limitations. Anonymization approach makes the data owners anonymous but vulnerable to attacks like linking attacks. Perturbation approach protects each and every attribute independently but unable to regenerate the original values from the perturbed data. Randomization technique provides good security for individual’s private data but the utility of the data. The degradation of the utility of the data is due to the noise added. The cryptographic technique provides good security for the data while providing high utility. But it falls short in efficiency when compared with other methods. Anyhow, there is no single privacy protecting algorithm capable of outperforming every other algorithm in all possible yardsticks. On the contrary, one algorithm may do well when compared to another, on a particular criterion. Novelty/Improvement: The paper presents various techniques which are used to perform PPDM technique and also tabulates their advantages and disadvantages.


Anonymization, Cryptography, Perturbation, Privacy Preserving Data Mining, Randomization.

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  • Ghalehsefidi, Narges J, Mohammad ND. A Hybrid Algorithm based on Heuristic Method to Preserve Privacy in Association Rule Mining. Indian Journal of Science and Technology. 2016 Jul; 9(27):1–10.
  • Benjamin CMF, Ke W, Rui C, Philip SY. Privacy-Preserving Data Publishing: A Survey of Recent Developments. ACM Computing Surveys. 2010 Jun; 42(4).
  • Charu CA, Philip SY. A General Survey of Privacy-Preserving Data Mining Models and Algorithms, Springer US. 2008; 11–52.
  • Privacy. Available from: Date Accessed: 29/09/2016.
  • A New Model for Privacy Preserving Sensitive Data Mining. Available from: Date Accessed: 26/07/2012.
  • Privacy Preserving Data Mining Techniques: Current Scenario and Future Prospects. Available from: Date Accessed: 23/11/2012.
  • Aruna Kumari D, Rajasekhara Rao K, Suman M. Privacy Preserving Data Mining. Springer International Publishing. 2014; 517–24.
  • Wang PS. Survey on Privacy Preserving Data Mining. International Journal of Digital Content Technology and its Applications. 2010; 4(9):1–7.
  • Latanya S. k-anonymity: a model for protecting privacy. International Journal on Uncertainty, Fuzziness and Knowledge based Systems. 2002 Oct; 10(5):557–70.
  • Yan Z, Ming D, Jiajin L, Yongcheng L. A Survey on Privacy Preserving Approaches in Data Publishing. IEEE Computer Society. 2009; 128–31.
  • Jinfei L, Jun L, Joshua ZH. Rating: Privacy Preservation for Multiple Attributes with Different Sensitivity requirements. Proceedings of 11th IEEE International Conference on Data Mining Workshops, China, IEEE. 2011. p. 666–73.
  • Kargupta H, Datta S, Wang Q, Krishnamoorthy S. On the Privacy Preserving Properties of Random Data Perturbation Techniques. Proceedings of the Third IEEE International Conference on Data Mining USA. 2003. p. 99.
  • Ashwin M, Johannes G, Daniel K, Muthuramakrishnan V. ℓ-Diversity: Privacy beyond k-Anonymity. ACM Transactions on Knowledge Discovery from Data. 2007 Mar; 1(1).
  • Ninghui L, Tiancheng L, Suresh V. t-Closeness: Privacy beyond K-Anonymity and l-Diversity. Proceedings of the IEEE 23rd International Conference on Data Engineering, Istanbul. 2007 Apr. p. 106–15.
  • Clifton C, Murat K, Jaideep V, Xiadong L, Michale YZ. Tools for privacy-preserving distributed data mining. ACM SIGKDD Explorations. 2002 Dec; 4(2):28–34.
  • Data Perturbation and Features Selection in Preserving Privacy. Available from: Date Accessed: 20/09/2012.
  • Andrew CCY. How to generate and exchange secrets. Proceedings of the 27th Annual Symposium on Foundations of Computer Science (FOCS). 1987. p. 218–29. PMid:3572436 PMCid:PMC1031495
  • Goldreich O, Micali S, Wigderson A. How to Play any Mental Game - A Completeness Theorem for Protocols with Honest Majority. Proceedings of the 19th Annual Symposium on the Theory of Computing, ACM, USA. 1987; 218–29.
  • Michale BO, Shafi G Wigderson A. Completeness theorems for non-cryptographic fault tolerant distributed computation, Proceedings of the 20th Annual Symposium on the Theory of Computing (STOC), ACM, Israel. 1988; 1–10.
  • Bhanumathi S, Sakthivel P. Preservation of Private Information using Secure Multi-Party Computation. Indian Journal of Science and Technology. 2016 Apr; 9(14):1–6.
  • Shimon E, Oded G, Abraham L. A Randomized Protocol for Signing Contracts. Communications of the ACM. 1985 Jun; 28(6):637–47.


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