Effective and Efficient Performance of Session Based Security Multiparty Collaborative Data Mining
Main Article Content
Abstract
With the advancements in E-commerce and E-governance where more personal data gets exchanged over internet, data privacy has become a key issue. Data mining is often defined as the process of discovering meaningful, new correlation patterns and trends through non-trivial extraction of implicit, previously unknown information from large amount of data stored in repositories and data mining is often carried out on internet based data and there is a need to protect private knowledge during data mining process. The process of preserving private knowledge during data mining is called as privacy preserving data mining (PPDM) [Yasien A.H, 2007]. The privacy preserving data mining is designed to fill up the gap between data mining and data confidentiality. When common users are involved in data mining, all users need to send their data to trusted common centre to conduct the mining; however, in situations with privacy concerns, it is very difficult for a user to trust the other users such a situation is called privacy preserving collaborative data mining. The proposed privacy preservation research work follows secure multiparty computation where multiple parties collaboratively get valid data mining results while disclosing no private data to each other or to any party who is not involved in the collaborative computations. The experiments are conducted to evaluate the privacy performance in terms of share participant, adversary resistance rate, communication round, number of participants and the session participants. The results clearly show that an improvement of nearly 12% of communication execution efficiency and 10% adversary resistance rate to the share participant effectiveness when compared to that of classical key models. This paper describes multiparty computing, (privacy preservation in collaborative data mining) cryptographic method for secured computation. The efficiency is achieved in terms of obtaining combined mining results from the participant private databases without disclosing individual’s private data.
Â
Keywords: Privacy Preserving Data Mining, Privacy Preserving Collaborative Data Mining, Secure Multiparty Computation
Downloads
Article Details
COPYRIGHT
Submission of a manuscript implies: that the work described has not been published before, that it is not under consideration for publication elsewhere; that if and when the manuscript is accepted for publication, the authors agree to automatic transfer of the copyright to the publisher.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work
- The journal allows the author(s) to retain publishing rights without restrictions.
- The journal allows the author(s) to hold the copyright without restrictions.