Abstract
Data mining, with its objective to efficiently discover valuable and inherent information from large databases, is particularly sensitive to misuse. Therefore an interesting new direction for data mining research is the development of techniques that incorporate privacy concerns and to develop accurate models without access to precise information in individual data records. The difficulty lies in the fact that the two metrics for evaluating privacy preserving data mining methods: privacy and accuracy are typically contradictory in nature. We address privacy preserving mining on distributed data in this paper and present an algorithm, based on the combination of probabilistic approach and cryptographic approach, to protect high privacy of individual information and at the same time acquire a high level of accuracy in the mining result.
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
Clifton, C., Marks, D.: Security and privacy implications of data mining. In: ACM SIGMOD Workshop on Research Issues on Data Mining and Knowledge Discovery (May 1996)
Estivill-Castro, V., Brankovic, L.: Data swapping: Balancing privacy against precision in mining for logic rules. In: Mohania, M., Tjoa, A.M. (eds.) DaWaK 1999. LNCS, vol. 1676, pp. 389–398. Springer, Heidelberg (1999)
Agrawal, R.: Data Mining: Crossing the Chasm. In: The 5th International Conference on Knowledge Discovery in Databases and Data Mining, San Diego, California (August 1999) (an invited talk at SIGKDD)
Agrawal, R., Srikant, R.: Privacy preserving data mining. In: Proc. of the ACM SIGMOD Conference on Management of Data, Dallas, Texas (May 2000)
Agrawal, D., Aggarwal, C.: On the Design and Quantification of Privacy Preserving Data Mining Algorithms. In: Proc. of 20th ACM Symp. on Principles of Database Systems (PODS) (2001)
Conway, R., Strip, D.: Selective partial access to a database. In: Proc. ACM Annual Conf. (1976)
Breiman, L., et al.: Classification and Regression Trees. Wadsworth, Belmont (1984)
Quinlan, J.R.: Induction of decision trees. Machine Learning 1, 81–106 (1986)
Evfimievski, A., et al.: Privacy Preserving Mining of Association Rules. Information Systems 29(4), 343–364 (2004)
Rizvi, S.J., Haritsa, J.R.: Maintaining Data Privacy in Association Rule Mining. In: Proc. 28th International Conf. Very Large Data Bases (2002)
Agrawal, R., Srikant, R.: Fast Algorithms for Mining Association Rules. In: IBM Almaden Research Center, San Jose, California (June 1994)
Adam, R., Wortman, J.C.: Security-control methods for statistical databases. ACM Computing Surveys 21(4), 515–556 (1989)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Shen, Hz., Zhao, Jd., Yao, R. (2006). Incorporating Privacy Concerns in Data Mining on Distributed Data. In: Euzenat, J., Domingue, J. (eds) Artificial Intelligence: Methodology, Systems, and Applications. AIMSA 2006. Lecture Notes in Computer Science(), vol 4183. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11861461_11
Download citation
DOI: https://doi.org/10.1007/11861461_11
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-40930-4
Online ISBN: 978-3-540-40931-1
eBook Packages: Computer ScienceComputer Science (R0)