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Policy-Based Autonomic Data Governance pp 42–65Cite as

Self-Generating Policies for Machine Learning in Coalition Environments

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Part of the Lecture Notes in Computer Science book series (LNISA,volume 11550)

Abstract

In any machine learning problem, obtaining and acquiring good training data is the main challenge that needs to be overcome to build a good model. When applying machine learning approaches in the context of coalition operations, one may only be able to get data for training machine learning models from coalition partners. However, all coalition partners may not be equally trusted, thus the task of deciding when, and when not, to accept training data for coalition operations remain complex. Policies can provide a mechanism for making these decisions but determining the right policies may be difficult given the variability of the environment. Motivated by this observation, in this paper, we propose an architecture that can generate policies required for building a machine learning model in a coalition environment without a significant amount of human input.

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Notes

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    http://www.alphaworks.ibm.com/tech/wpml.

  2. 2.

    http://ufldl.stanford.edu/housenumbers/.

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    http://scikit-learn.org.

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Acknowledgments

This research was sponsored by the U.S. Army Research Laboratory and the U.K. Ministry of Defence under Agreement Number W911NF-16-3-0001. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the U.S. Army Research Laboratory, the U.S. Government, the U.K. Ministry of Defence or the U.K. Government. The U.S. and U.K. Governments are authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation hereon.

Dstl/CP107670. Content includes material subject to Crown copyright (2018), Dstl. This information is licensed under the terms of the Open Government Licence except where otherwise stated. To view this licence, visit http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3 or write to the Information Policy Team, The National Archives, Kew, London TW9 4DU, or email: psi@nationalarchives.gsi.gov.uk.

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Authors and Affiliations

  1. Distributed AI Department, IBM TJ Watson Research Center, Yorktown Heights, NY, 10549, USA

    Dinesh Verma, Seraphin Calo, Shonda Witherspoon & Irene Manotas

  2. Computer Science Department, Purdue University, West Lafayette, IN, 47907, USA

    Elisa Bertino & Amani M. Abu Jabal

  3. Army Research Labs, 2800 Powder Mill Road, Adelphi, MD, 20783, USA

    Greg Cirincione & Ananthram Swami

  4. Defence Science and Technology Laboratory, Ministry of Defence, Porton Down, Salisbury, SP4 0JQ, UK

    Gavin Pearson

  5. IBM Research UK, Hartree Center, Warrington, UK

    Geeth de Mel

Authors
  1. Dinesh Verma
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  2. Seraphin Calo
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  3. Shonda Witherspoon
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  4. Irene Manotas
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  5. Elisa Bertino
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  6. Amani M. Abu Jabal
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  7. Greg Cirincione
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  8. Ananthram Swami
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  9. Gavin Pearson
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  10. Geeth de Mel
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Corresponding author

Correspondence to Seraphin Calo .

Editor information

Editors and Affiliations

  1. IBM Research, Yorktown Heights, NY, USA

    Seraphin Calo

  2. Purdue University, West Lafayette, IN, USA

    Prof. Dr. Elisa Bertino

  3. IBM Research, Yorktown Heights, NY, USA

    Prof. Dinesh Verma

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Verma, D. et al. (2019). Self-Generating Policies for Machine Learning in Coalition Environments. In: Calo, S., Bertino, E., Verma, D. (eds) Policy-Based Autonomic Data Governance. Lecture Notes in Computer Science(), vol 11550. Springer, Cham. https://doi.org/10.1007/978-3-030-17277-0_3

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  • DOI: https://doi.org/10.1007/978-3-030-17277-0_3

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