A collaborative framework for Distributed Privacy-Preserving Support Vector Machine learning.

TitleA collaborative framework for Distributed Privacy-Preserving Support Vector Machine learning.
Publication TypeConference Proceedings
Year of Publication2012
AuthorsQue, J, Jiang, X, Ohno-Machado, L
Conference NameAMIA Annu Symp Proc
Volume2012
Pagination1350-9
Date Published2012
ISBN1942-597X
iDASH CategoryPrivacy Technology
AbstractA Support Vector Machine (SVM) is a popular tool for decision support. The traditional way to build an SVM model is to estimate parameters based on a centralized repository of data. However, in the field of biomedicine, patient data are sometimes stored in local repositories or institutions where they were collected, and may not be easily shared due to privacy concerns. This creates a substantial barrier for researchers to effectively learn from the distributed data using machine learning tools like SVMs. To overcome this difficulty and promote efficient information exchange without sharing sensitive raw data, we developed a Distributed Privacy Preserving Support Vector Machine (DPP-SVM). The DPP-SVM enables privacy-preserving collaborative learning, in which a trusted server integrates "privacy-insensitive" intermediary results. The globally learned model is guaranteed to be exactly the same as learned from combined data. We also provide a free web-service (http://privacy.ucsd.edu:8080/ppsvm/) for multiple participants to collaborate and complete the SVM-learning task in an efficient and privacy-preserving manner.
PubMed ID23304414