Matrix analysis
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
Foundations of Cryptography: Basic Tools
Foundations of Cryptography: Basic Tools
Tools for privacy preserving distributed data mining
ACM SIGKDD Explorations Newsletter
Achieving k-anonymity privacy protection using generalization and suppression
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
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
State-of-the-art in privacy preserving data mining
ACM SIGMOD Record
Detecting privacy and ethical sensitivity in data mining results
ACSC '04 Proceedings of the 27th Australasian conference on Computer science - Volume 26
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
Clustering classifiers for knowledge discovery from physically distributed databases
Data & Knowledge Engineering
Privacy-Preserving Distributed Mining of Association Rules on Horizontally Partitioned Data
IEEE Transactions on Knowledge and Data Engineering
Privacy-Preserving Data Mining: Why, How, and When
IEEE Security and Privacy
Knowledge discovery by probabilistic clustering of distributed databases
Data & Knowledge 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
Data swapping: a risk-utility framework and web service implementation
dg.o '03 Proceedings of the 2003 annual national conference on Digital government research
L-diversity: Privacy beyond k-anonymity
ACM Transactions on Knowledge Discovery from Data (TKDD)
Anonymizing Classification Data for Privacy Preservation
IEEE Transactions on Knowledge and Data Engineering
Maintaining data privacy in association rule mining
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Impossibility of unconditionally secure scalar products
Data & Knowledge Engineering
Extending l-diversity to generalize sensitive data
Data & Knowledge Engineering
Privacy-aware collection of aggregate spatial data
Data & Knowledge Engineering
On the effectiveness of distributed learning on different class-probability distributions of data
CAEPIA'11 Proceedings of the 14th international conference on Advances in artificial intelligence: spanish association for artificial intelligence
Arbitrarily distributed data-based recommendations with privacy
Data & Knowledge Engineering
Privacy-preserving back-propagation and extreme learning machine algorithms
Data & Knowledge Engineering
| Hi-index | 0.03 |
Privacy concerns over sensitive data have become important in knowledge discovery. Usually, data owners have different levels of concerns over different data attributes, which adds complexity to data privacy. Moreover, collusion among malicious adversaries poses a severe threat to data security. In this paper, we present an efficient clustering method for distributed multi-party data sets using the orthogonal transformation and perturbation techniques. Our method allows data owners to apply different levels of privacy to different attributes. The miner, while receiving the perturbed data, can still obtain accurate clustering results. This method protects data privacy, not only in the semi-honest situation, but also in the presence of collusion. The accuracy of the mining results and the privacy levels, and their relations to the parameters in the method are analyzed. Moreover, we propose an improved version of the method to alleviate the problem with a large number of participants. Experimental results demonstrate the effectiveness of our method as compared to existing methods.