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A local asynchronous distributed privacy preserving feature selection algorithm for large peer-to-peer networks

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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 interactions among participating nodes. We present results on real-world dataset in order to test the performance of the proposed algorithm.

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Authors and Affiliations

  1. Stinger Ghaffarian Technologies Inc., IDU Group, NASA Ames Research Center, Moffett Field, CA, 94035, USA

    Kamalika Das

  2. Mission Critical Technologies Inc., IDU Group, NASA Ames Research Center, Moffett Field, CA, 94035, USA

    Kanishka Bhaduri

  3. Department of CSEE, University of Maryland, Baltimore County, Baltimore, MD, 21250, USA

    Hillol Kargupta

  4. AGNIK LLC, Baltimore, MD, USA

    Hillol Kargupta

Authors
  1. Kamalika Das
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  2. Kanishka Bhaduri
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  3. Hillol Kargupta
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Correspondence to Kamalika Das.

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Das, K., Bhaduri, K. & Kargupta, H. A local asynchronous distributed privacy preserving feature selection algorithm for large peer-to-peer networks. Knowl Inf Syst 24, 341–367 (2010). https://doi.org/10.1007/s10115-009-0274-3

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  • DOI: https://doi.org/10.1007/s10115-009-0274-3

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