Abstract
Privacy concerns on sensitive data are now becoming indispensable in data mining and knowledge discovering. Data owners usually have different concerns for different data attributes. Meanwhile the collusion among malicious adversaries produces a severe threat to the security of data.
In this paper, we present an efficient method to generate the attribute-wised orthogonal matrix for data transformation. Moreover, we introduce a privacy preserving method for clustering problem in multi-party condition. Our method can not only protect data in the semi-honest model but also in the malicious one. We also analyze the accuracy of the results, the privacy levels obtained, and their relations with the parameters in our method.
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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
Agrawal, R., et al.: Privacy-preserving data mining. In: Proc. of the ACM SIGMOD Conference on Management of Data (2000)
Lindell, Y., Pinkas, B.: Privacy Preserving Data Mining. In: Bellare, M. (ed.) CRYPTO 2000. LNCS, vol. 1880, p. 36. Springer, Heidelberg (2000)
Rizvi, S., et al.: Maintaining data privacy in association rule mining. In: Proc. of the 28th Conference on Very Large Data Bases (2002)
Evfimievski, A., et al.: Privacy preserving mining of association rules. Information Systems 29(4), 343–364 (2004)
Xia, Y., et al.: Mining association rules with non-uniform privacy concerns. In: Proc. of the 9th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery (2004)
Du, W., et al.: Using randomized response techniques for privacy-preserving data mining. In: Proc. of the 9th ACM SIGKDD international conference on Knowledge discovery and data mining (2003)
Warner, S.: Randomized response: A survey technique for eliminating evasive answer bias. Journal of the American Statistical Association 60, 63–69 (1965)
Oliveira, S., et al.: Privacy preserving clustering by data transformation. In: Proc. of the 18th Brazilian Symposium on Databases (2003)
Chen, K., et al.: Privacy preserving data classification with rotation perturbation. In: Proc. of the 5th IEEE International Conference on Data Mining (2005)
Liu, K., et al.: Random projection-based multiplicative data perturbation for privacy preserving distributed data mining. IEEE Transactions on Knowlege and Data Engineering 18(1), 92–106 (2006)
Vaidya, J., et al.: Privacy-preserving k-means clustering over vertically partitioned data. In: Proc. of the 9th ACM SIGKDD international conference on Knowledge discovery and data mining (2003)
Vaidya, J., et al.: Privacy preserving association rule mining in vertically partitioned data. In: Proc. of the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2002)
Kantarcioglu, M., et al.: Privacy-preserving distributed mining of association rules on horizontally partitioned data. IEEE Transactions on Knowlege and Data Engineering 16(9), 1026–1037 (2004)
Wright, R., et al.: Privacy-preserving bayesian network structure computation on distributed heterogeneous data. In: Proc. of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2004)
Agrawal, D., et al.: On the design and quantification of privacy preserving data mining algorithms. In: Proc. of the 20th ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems (2001)
Stewart, G.: The efficient generation of random orthogonal matrices with an application to condition estimators. SIAM Journal on Numerical Analysis 17(3), 403–409 (1980)
Hettich, S., et al.: The uci kdd archive. Univeristy of California, Irvine, Department of Information and Computer Science (1999)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Yang, W., Huang, S. (2007). Privacy Preserving Clustering for Multi-party. In: Kotagiri, R., Krishna, P.R., Mohania, M., Nantajeewarawat, E. (eds) Advances in Databases: Concepts, Systems and Applications. DASFAA 2007. Lecture Notes in Computer Science, vol 4443. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71703-4_20
Download citation
DOI: https://doi.org/10.1007/978-3-540-71703-4_20
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-71702-7
Online ISBN: 978-3-540-71703-4
eBook Packages: Computer ScienceComputer Science (R0)