Privacy-preserving ranking over vertically partitioned data

Abstract

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.