A local asynchronous distributed privacy preserving feature selection algorithm for large peer-to-peer networks 一种大型对等网络的局部异步分布式必威体育官网网址特征选择算法.pdf

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A local asynchronous distributed privacy preserving feature selection algorithm for large peer-to-peer networks 一种大型对等网络的局部异步分布式必威体育官网网址特征选择算法.pdf

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Knowl Inf Syst (2010) 24:341–367 DOI 10.1007/s10115-009-0274-3 REGULAR PAPER A local asynchronous distributed privacy preserving feature selection algorithm for large peer-to-peer networks Kamalika Das · Kanishka Bhaduri · Hillol Kargupta Received: 15 August 2008 / Revised: 17 July 2009 / Accepted: 10 October 2009 / Published online: 25 November 2009 © Springer-Verlag London Limited 2009 Abstract In this paper we develop a local distributed privacy preserving algorithm for feature selection in a large peer-to-peer environment. Feature selection is often used in machine learning for data compaction and efﬁcient learning by eliminating the curse of dimensionality. There exist many solutions for feature selection when the data are located at a central location. However, it becomes extremely challenging to perform the same when the data are distributed across a large number of peers or machines. Centralizing the entire dataset or portions of it can be very costly and impractical because of the large number of data sources, the asynchronous nature of the peer-to-peer networks, dynamic nature of the data/network, and privacy concerns. The solution proposed in this paper allows us to perform feature selection in an asynchronous fashion with a low communication overhead where each peer can specify its own privacy constraints. The algorithm works based on local interac- tions among participating nodes. We present results on real-world dataset in order to test the performance of the proposed algorithm. Keywords Privacy preserving · Data mining · Feature selection · Distributed computation K. Das ( ) B Stinger Ghaffarian Technologies Inc., IDU Group, NASA Ames Research Center, Moffett Field, CA 94035, USA e-mail: kdas1@; kamalika_das@ K. Bhaduri Mission Critical Technologies Inc., IDU Group, NASA Ames Research Center, Moffett Field, CA 94035, USA e-mail: Kanishka.Bhaduri-1@ H. Kargupta Dep