NDLI: Privacy Aware Non-linear Support Vector Machine for Multi-source Big Data

Content Provider	IEEE Xplore Digital Library
Author	Yunmei Lu Phoungphol, P. Yanqing Zhang
Copyright Year	2014
Description	Author affiliation: Dept. of Comput. Sci., Georgia State Univ., Atlanta, GA, USA (Yunmei Lu; Phoungphol, P.; Yanqing Zhang)
Abstract	In order to build reliable prediction models and attain high classification accuracy, assembling datasets from multiple databases maintained by different sources (such as different hospitals) has become increasingly common. However, assembling these composite datasets involves the disclosure of individuals' records, therefore many local owners are reluctant to share their data due to privacy concerns. This paper presents a framework for building a Privacy-Aware Non-linear Support Vector Machine (PAN-SVM) classifier using distributed data sources. The framework with three layers can do global classification based on distributed data sources and protect individuals' records at the same time. At the bottom layer, k-means clustering is used to select landmarks that will be used by the medium layer after they are encrypted by a secure sum protocol. The medium layer employs Nystrom low-rank approximation and kernel matrix decomposition techniques to construct a global SVM classifier which is accelerated at the top layer by employing a cutting-plane technique. Simulation results on multiple datasets indicate that the new framework can solve the classification problem on distributed data sources effectively and efficiently, and protect the privacy of individuals' data as well.
Starting Page	783
Ending Page	789
File Size	335003
Page Count	7
File Format	PDF
ISBN	9781479965137
DOI	10.1109/TrustCom.2014.103
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-09-24
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Data privacy Cutting-plane Method Privacy preserving Multi-source Data SVM Low-rank Approximation Distributed data-mining Matrix Decomposition Support vector machines Training Accuracy Distributed databases Data models Kernel
Content Type	Text
Resource Type	Article

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