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
Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
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
State-of-the-art in privacy preserving data mining
ACM SIGMOD Record
Privacy-preserving distributed k-means clustering over arbitrarily partitioned data
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Privacy Preserving Data Mining
Privacy Preserving Data Mining
Privacy-Preserving SVM classification on vertically partitioned data
PAKDD'06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Privacy-Preserving decision trees over vertically partitioned data
DBSec'05 Proceedings of the 19th annual IFIP WG 11.3 working conference on Data and Applications Security
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Privacy concerns in many application domains prevents sharing of data, which limits data mining technology to identify patterns and trends from large amount of data. Traditional data mining algorithms have been developed within a centralized model. However, distributed knowledge discovery has been proposed by many researchers as a solution to privacy preserving data mining techniques. By vertically partitioned data, each site contains some attributes of the entities in the environment. Once an existing data mining technique is executed at each site independently, the local results need to be combined to produce the globally valid result. Learning how to rank existing entities is a central part in many knowledge discovery problems. In this paper, we present a method for ranking problem based on SVMRank algorithm in situations where different sites contain different attributes for a common set of entities. Each site learns the ranking model of entities without knowing the attributes in other sites and at the end the global rank model will be built.