Open Access Open Access  Restricted Access Subscription Access

An Empirical Study on Preserving Sensitive Knowledge in Data Mining


Affiliations
1 Department of Software Systems, Sri Krishna Arts and Science College, Coimbatore – 641008, Tamil Nadu, India
 

Protection of data in data mining involves preserving sensitive data and sensitive knowledge. Preserving sensitive data focus on protecting the data when it is shared for mining with third parties. In some situations even the result of data mining techniques might reveal sensitive information. This area of research which focuses on output preservation of data mining techniques is termed as Preserving Sensitive Knowledge. The sensitive results of data mining are preserved by reducing the efficiency of the result. In this paper, an empirical study on existing sensitive knowledge preservation approaches is discussed. The approaches include rule hiding in association rule mining and classification technique. The output data protection also includes the restriction in query processing. The article highlights the merits and demerits on each approach.


Keywords

Association Rules, Privacy Preservation, Rule Hiding, Sensitive Knowledge.
User
Notifications
Font Size


  • Aggarwal CC, Yu PS. A general survey of privacy-preserving data mining models and algorithms. Privacy-preserving Data Mining; 2008. p. 11–52. https://doi.org/10.1007/978-0-38770992-5_2
  • Agrawal D, Aggarwal CC. On the design and quantification of privacy preserving data mining algorithms. Proceedings of the Twentieth ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems; 2001. p. 247–55. https://doi.org/10.1145/375551.375602
  • Chen K, Liu L. A survey of multiplicative perturbation for privacy-preserving data mining. Privacy-Preserving Data Mining. 2008. p. 157–81. https://doi.org/10.1007/978-0-38770992-5_7
  • Warner SL. Randomized response: A survey technique for eliminating evasive answer bias. Journal of the American Statistical Association. 1965; 60(309):63–9. https://doi.org/10.1080/01621459.1965.10480775
  • Refaat M, Aboelseoud H, Shafee K, Badr M. Privacy preserving association rule hiding techniques: Current research challenges. International Journal of Computer Applications. 2016; 136(6):11–7. https://doi.org/10.5120/ijca2016908446
  • Telikani A, Shahbahrami A. Data sanitization in association rule mining: An analytical review. Expert Systems with Applications. 2018; 96:406–26. https://doi.org/10.1016/j.eswa.2017.10.048
  • Garg V, Singh A, Singh D. A survey of association rule hiding algorithms. 2014 Fourth Intenational Conference on Communication Systems and Network Technologies, IEEE; 2014. p. 404–7. PMid: 25657953 PMCid: PMC4311352. https://doi.org/10.1109/CSNT.2014.86
  • Natwichai J, Li X, Orlowska M. Hiding classification rules for data sharing with privacy preservation. International Conference on Data Warehousing and Knowledge Discovery; Springer; Berlin, Heidelberg. 2005 Aug. p. 468–77). https://doi.org/10.1007/11546849_46
  • Dwork C, Kenthapadi K, Mcsherry F, Mironov I, Naor M. Our data, ourselves: Privacy via distributed noise generation. Advances in Cryptology-EUROCRYPT; 2006. p. 486–503. https://doi.org/10.1007/11761679_29
  • Thong T, Buttyan L. Query auditing for protecting max/min values of sensitive attributes in statistical databases. Trust, Privacy and Security in Digital Business. 2012. p. 192–206. https://doi.org/10.1007/978-3-642-32287-7_17

Abstract Views: 232

PDF Views: 119




  • An Empirical Study on Preserving Sensitive Knowledge in Data Mining

Abstract Views: 232  |  PDF Views: 119

Authors

S. Dhanalakshmi
Department of Software Systems, Sri Krishna Arts and Science College, Coimbatore – 641008, Tamil Nadu, India

Abstract


Protection of data in data mining involves preserving sensitive data and sensitive knowledge. Preserving sensitive data focus on protecting the data when it is shared for mining with third parties. In some situations even the result of data mining techniques might reveal sensitive information. This area of research which focuses on output preservation of data mining techniques is termed as Preserving Sensitive Knowledge. The sensitive results of data mining are preserved by reducing the efficiency of the result. In this paper, an empirical study on existing sensitive knowledge preservation approaches is discussed. The approaches include rule hiding in association rule mining and classification technique. The output data protection also includes the restriction in query processing. The article highlights the merits and demerits on each approach.


Keywords


Association Rules, Privacy Preservation, Rule Hiding, Sensitive Knowledge.

References





DOI: https://doi.org/10.15613/sijrs%2F2018%2Fv5i1%2F188667