Data Confidentiality Versus Chase
We present a generalization of a strategy, called SCIKD, proposed in  that allows to reduce a disclosure risk of confidential data in an information system S  using methods based on knowledge discovery. The method proposed in  protects confidential data against Rule-based Chase, the null value imputation algorithm driven by certain rules , . This method identifies a minimal subset of additional data in S which needs to be hidden to guarantee that the confidential data are not revealed by Chase. In this paper we propose a bottom-up strategy which identifies, for each object x in S, a maximal set of values of attributes which do not have to be hidden and still the information associated with secure attribute values of x is protected. It is achieved without examining all possible combinations of values of attributes. Our method is driven by classification rules extracted from S and takes into consideration their confidence and support.
KeywordsAssociation Rule Transitive Closure Disclosure Risk Imputation Algorithm Incomplete Information System
Unable to display preview. Download preview PDF.
- 1.UCI Machine Learning Rep., http://www.ics.uci.edu/~mlearn/MLRepository.html
- 2.Dardzińska, A., Raś, Z.W.: Rule-Based Chase Algorithm for Partially Incomplete Information Systems. In: Tsumoto, S., et al. (eds.) AM 2003. LNCS (LNAI), vol. 3430, pp. 42–51. Springer, Heidelberg (2005)Google Scholar
- 3.Dardzińska, A., Raś, Z.W.: On Rules Discovery from Incomplete Information Systems. In: Lin, T.Y., et al. (eds.) Proceedings of ICDM’03 Workshop on Foundations and New Directions of Data Mining, Melbourne, Florida, pp. 31–35. IEEE Computer Society Press, Los Alamitos (2003)Google Scholar
- 4.Dardzińska, A., Raś, Z.W.: Chasing Unknown Values in Incomplete Information Systems. In: Lin, T.Y., et al. (eds.) Proceedings of ICDM’03 Workshop on Foundations and New Directions of Data Mining, Melbourne, Florida, pp. 24–30. IEEE Computer Society Press, Los Alamitos (2003)Google Scholar
- 5.Du, W., Atallah, M.J.: Secure Multi-party Computation Problems and their Applications: a review and open problems. In: New Security Paradigms Workshop (2001)Google Scholar
- 6.Du, W., Zhan, Z.: Building decision tree classifier on private data. In: Proceedings of the IEEE ICDM Workshop on Privacy, Security and Data Mining (2002)Google Scholar
- 7.Im, S., Raś, Z.W., Dardzińska, A.: SCIKD: Safeguarding Classified Information from Knowledge Discovery. In: Foundations of Semantic Oriented Data and Web Mining, Proceedings of 2005 IEEE ICDM Workshop, Houston, Texas, pp. 34–39. Math. Dept., Saint Mary’s Univ., Nova Scotia, Canada (2005)Google Scholar
- 8.Kantarcioglou, M., Clifton, C.: Privacy-preserving distributed mining of association rules on horizontally partitioned data. In: Proceedings of the ACM SIGMOD Workshop on Research Isuues in Data Mining and Knowledge Discovery, pp. 24–31 (2002)Google Scholar
- 9.Oliveira, S.R.M., Zaiane, O.R.: Privacy preserving frequent itemset mining. In: Proceedings of the IEEE ICDM Workshop on Privacy, Security and Data Mining, pp. 43–54 (2002)Google Scholar
- 12.Yao, A.C.: How to generate and exchange secrets. In: Proceedings of the 27th IEEE Symposium on Foundations of Computer Science, pp. 162–167 (1996)Google Scholar