Self Generating Policies for Machine Learning in Coalition Environments

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.
  • Dinesh Verma (IBM US)
  • Seraphin Calo (IBM US)
  • Shonda Witherspoon (IBM US)
  • Irene Manatos (IBM US)
  • Elisa Bertino (Purdue)
  • Amani Abu Jabal (Purdue)
  • Greg Cirincione (ARL)
  • Ananthram Swami (ARL)
  • Gavin Pearson (Dstl)
  • Geeth de Mel (IBM UK)
Date Apr-2019
Venue Policy-Based Autonomic Data Governance, pp. 42-65. Springer, Cham, 2019. [link]