Efficient private bidding and auctions with an oblivious third party
CCS '99 Proceedings of the 6th ACM conference on Computer and communications security
Privacy-preserving data mining
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Privacy Preserving Data Mining
CRYPTO '00 Proceedings of the 20th Annual International Cryptology Conference on Advances in Cryptology
Privacy-preserving k-means clustering over vertically partitioned data
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Privacy-preserving distributed k-means clustering over arbitrarily partitioned data
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Maintaining data privacy in association rule mining
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Protocols for secure computations
SFCS '82 Proceedings of the 23rd Annual Symposium on Foundations of Computer Science
ESORICS'05 Proceedings of the 10th European conference on Research in Computer Security
On private scalar product computation for privacy-preserving data mining
ICISC'04 Proceedings of the 7th international conference on Information Security and Cryptology
Privacy preserving BIRCH algorithm for clustering over vertically partitioned databases
SDM'06 Proceedings of the Third VLDB international conference on Secure Data Management
Privacy preserving distributed DBSCAN clustering
Proceedings of the 2012 Joint EDBT/ICDT Workshops
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In this paper we address the issue of privacy preserving clustering. Specially, we consider a scenario in which two parties owning confidential databases wish to run a clustering algorithm on the union of their databases, without revealing any unnecessary information. This problem is a specific example of secure multi-party computation and as such, can be solved using known generic protocols. However there are several clustering algorithms are available. They are applicable to specific type of data, but DBSCAN [4] is applicable for all types of data and the clusters obtained by DBSCAN are similar to natural clusters. However, DBSCAN [4] algorithm is basically designed as an algorithm working on a single database. In this paper we proposed a protocols for how the distances are measured between data points, when the data is distributed across two parties. By using these protocols we propose the first novel method for running DBSCAN algorithm operating over vertically and horizontally partitioned data sets, distributed in two different databases in a privacy preserving manner.