Abstract
In multiparty collaborative data mining, participants contribute their own data sets and hope to collaboratively mine a comprehensive model based on the pooled data set. How to efficiently mine a quality model without breaching each party's privacy is the major challenge. In this paper, we propose an approach based on geometric data perturbation and data mining service-oriented framework. The key problem of applying geometric data perturbation in multiparty collaborative mining is to securely unify multiple geometric perturbations that are preferred by different parties, respectively. We have developed three protocols for perturbation unification. Our approach has three unique features compared to the existing approaches: with geometric data perturbation, these protocols can work for many existing popular data mining algorithms, while most of other approaches are only designed for a particular mining algorithm; both the two major factors: data utility and privacy guarantee are well preserved, compared to other perturbation-based approaches; and two of the three proposed protocols also have great scalability in terms of the number of participants, while many existing cryptographic approaches consider only two or a few more participants. We also study different features of the three protocols and show the advantages of different protocols in experiments.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
More From: IEEE Transactions on Parallel and Distributed Systems
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.