Data mining: concepts and techniques
Data mining: concepts and techniques
Random projection in dimensionality reduction: applications to image and text data
Proceedings of the seventh 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
Clustering Large Graphs via the Singular Value Decomposition
Machine Learning
Deriving private information from randomized data
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Privacy-preserving distributed k-means clustering over arbitrarily partitioned data
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Comparing clusterings: an axiomatic view
ICML '05 Proceedings of the 22nd international conference on Machine learning
IEEE Transactions on Knowledge and Data Engineering
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
How to generate and exchange secrets
SFCS '86 Proceedings of the 27th Annual Symposium on Foundations of Computer Science
An attacker's view of distance preserving maps for privacy preserving data mining
PKDD'06 Proceedings of the 10th European conference on Principle and Practice of Knowledge Discovery in Databases
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This paper proposes an efficient solution with high accuracy to the problem of privacy-preserving clustering. This problem has been studied mainly using two approaches: data perturbation and secure multiparty computation. In our research, we focus on the data perturbation approach, and propose an algorithm of linear time complexity based on 1-d clustering to perturb the data. Performance study on real datasets from the UCI machine learning repository shows that our approach reaches better accuracy and hence lowers the distortion of clustering result than previous approaches.