Abstract
Data mining technology can help extract useful knowledge from large data sets. The process of data collection and data dissemination may, however, result in an inherent risk of privacy threats. In this paper, the SIF-IDF algorithm is proposed to modify original databases in order to hide sensitive itemsets. It is a greedy approach based on the concept of the Term Frequency and Inverse Document Frequency (TF-IDF) borrowed from text mining. Experimental results also show the performance of the proposed approach.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Amiri A.: Dare to share: Protecting sensitive knowledge with data sanitization. Decision Support Systems, 181–191 (2007)
Atallah, M., Bertino, E., Elmagarmid, A., Ibrahim, M., Verykios, V.S.: Disclosure limitation of sensitive rules. In: Knowledge and Data Engineering Exchange Workshop, pp. 45–52 (1999)
Agrawal, R., Imielinski, T., Sawmi, A.: Mining association rules between sets of items in large databases. In: The ACM SIGMOD Conference on Management of Data, pp. 207–216 (1993)
Agrawal, R., Srikant, R.: Fast algorithm for mining association rules. In: The International Conference on Very Large Data Bases, pp. 487–499 (1994)
Dasseni, E., Verykios, V.S., Elmagarmid, A.K., Bertino, E.: Hiding Association Rules by Using Confidence and Support. In: The 4th International Workshop on Information Hiding, pp. 369–383 (2001)
Garey, M.R., Johnson, D.S.: Computers and Intractability: A Guide to the Theory of NP-Conpleteness. W. H. Freeman, New York (1979)
Leary D. E. O.: Knowledge Discovery as a Threat to Database Security. Knowledge Discovery in Databases, 507–516 (1991)
Pontikakis, E.D., Tsitsonis, A.A., Verykios, V.S.: An experimental study of distortion-based techniques for association rule hiding. In: 18th Conference on Database Security, pp. 325–339 (2004)
Salton, G., Fox, E.A., Wu, H.: Extended Boolean information retrieval. Communications of the ACM 26(2), 1022–1036 (1983)
Verykios, V.S., Elmagarmid, A., Bertino, E., Saygin, Y., Dasseni, E.: Association Rule Hiding. IEEE Transactions on knowledge and Data Engineering 16(4), 434–447 (2004)
Zhu, Z., Wang, G., Du, W.: Deriving Private Information from Association Rule Mining Results. In: IEEE International Conference on Data Engineering, pp. 18–29 (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Hong, TP., Lin, CW., Yang, KT., Wang, SL. (2011). A Heuristic Data-Sanitization Approach Based on TF-IDF. In: Mehrotra, K.G., Mohan, C.K., Oh, J.C., Varshney, P.K., Ali, M. (eds) Modern Approaches in Applied Intelligence. IEA/AIE 2011. Lecture Notes in Computer Science(), vol 6703. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21822-4_17
Download citation
DOI: https://doi.org/10.1007/978-3-642-21822-4_17
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-21821-7
Online ISBN: 978-3-642-21822-4
eBook Packages: Computer ScienceComputer Science (R0)