Abstract: “Recently, many data mining algorithms for discovering and exploiting patterns in data are developed and the amount of data about individuals that is collected and stored continues to rapidly increase. However, databases containing information about individuals may be sensitive and data mining algorithms run on such data sets may violate individual privacy. Also most organizations collect and share information for their specific needs very frequently. In such cases it is important for each organization to make sure that the privacy of the individual is not violated or sensitive information is not revealed. In this paper we have proposed a novel method to provide privacy to the data when the data is vertically partitioned and distributed over sites. In this work we presented trusted third party framework along with an application that generates k-anonymous dataset from two vertically partitioned sources without disclosing data from one site to other. K- anonymity constraint is satisfied”

Keywords: Distributed data mining; Privacy preserving; kanonymity; Genetic algorithm