Privacy-preserving data mining
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Privacy preserving association rule mining in vertically partitioned data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Building decision tree classifier on private data
CRPIT '14 Proceedings of the IEEE international conference on Privacy, security and data mining - Volume 14
Privacy-preserving Distributed Clustering using Generative Models
ICDM '03 Proceedings of the Third IEEE International Conference on 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
IEEE Transactions on Knowledge and Data Engineering
Secure and private sequence comparisons
Proceedings of the 2003 ACM workshop on Privacy in the electronic society
Privacy-Preserving Distributed Mining of Association Rules on Horizontally Partitioned Data
IEEE Transactions on Knowledge and Data Engineering
Distributed clustering based on sampling local density estimates
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Privacy preserving spatio-temporal clustering on horizontally partitioned data
DaWaK'06 Proceedings of the 8th international conference on Data Warehousing and Knowledge Discovery
ESORICS'05 Proceedings of the 10th European conference on Research in Computer Security
Designing evolving user profile in e-CRM with dynamic clustering of Web documents
Data & Knowledge Engineering
Privately detecting bursts in streaming, distributed time series data
Data & Knowledge Engineering
Privacy-preserving data publishing for cluster analysis
Data & Knowledge Engineering
Arbitrarily distributed data-based recommendations with privacy
Data & Knowledge Engineering
Efficient privacy preserving k-means clustering
PAISI'10 Proceedings of the 2010 Pacific Asia conference on Intelligence and Security Informatics
Privacy-preserving back-propagation and extreme learning machine algorithms
Data & Knowledge Engineering
The research on Fisher-RBF data fusion model of network security detection
ISNN'12 Proceedings of the 9th international conference on Advances in Neural Networks - Volume Part II
A Knowledge Model Sharing Based Approach to Privacy-Preserving Data Mining
Transactions on Data Privacy
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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Data mining has been a popular research area for more than a decade due to its vast spectrum of applications. However, the popularity and wide availability of data mining tools also raised concerns about the privacy of individuals. The aim of privacy preserving data mining researchers is to develop data mining techniques that could be applied on databases without violating the privacy of individuals. Privacy preserving techniques for various data mining models have been proposed, initially for classification on centralized data then for association rules in distributed environments. In this work, we propose methods for constructing the dissimilarity matrix of objects from different sites in a privacy preserving manner which can be used for privacy preserving clustering as well as database joins, record linkage and other operations that require pair-wise comparison of individual private data objects horizontally distributed to multiple sites. We show communication and computation complexity of our protocol by conducting experiments over synthetically generated and real datasets. Each experiment is also performed for a baseline protocol, which has no privacy concern to show that the overhead comes with security and privacy by comparing the baseline protocol and our protocol.