Privacy-preserving data mining
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
On the design and quantification of privacy preserving data mining algorithms
PODS '01 Proceedings of the twentieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Privacy preserving association rule mining in vertically partitioned data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Privacy-preserving k-means clustering over vertically partitioned data
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Using randomized response techniques for privacy-preserving data mining
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Privacy preserving mining of association rules
Information Systems - Knowledge discovery and data mining (KDD 2002)
Mining association rules with non-uniform privacy concerns
Proceedings of the 9th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery
Privacy-preserving Bayesian network structure computation on distributed heterogeneous data
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Privacy-Preserving Distributed Mining of Association Rules on Horizontally Partitioned Data
IEEE Transactions on Knowledge and Data Engineering
IEEE Transactions on Knowledge and Data Engineering
Privacy Preserving Data Classification with Rotation Perturbation
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Maintaining data privacy in association rule mining
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Bands of privacy preserving objectives: classification of PPDM strategies
AusDM '11 Proceedings of the Ninth Australasian Data Mining Conference - Volume 121
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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.