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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)


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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  • DOI: 10.1007/978-3-030-17277-0_3
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  1. Rasch, R., Kott, A., Forbus, K.D.: AI on the battlefield: an experimental exploration. In: AAAI/IAAI, pp. 906–912 (2002)

    Google Scholar 

  2. Cortes, C., Jackel, L.D., Solla, S.A., Vapnik, V., Denker, J.S.: Learning curves: asymptotic values and rate of convergence. In: Advances in Neural Information Processing Systems, pp. 327–334 (1994)

    Google Scholar 

  3. Perlich, C., Provost, F., Simonoff, J.S.: Tree induction vs. logistic regression: a learning-curve analysis. J. Mach. Learn. Res. 4(Jun), 211–255 (2003)

    MathSciNet  MATH  Google Scholar 

  4. Verma, D., et al.: Generative policy model for autonomic management. In: 2017 IEEE SmartWorld, Distributed Analytics InfraStructure and Algorithms for Multi-organization Federations Workshop. IEEE (2017)

    Google Scholar 

  5. Cirincione, G., Verma, D., Bertino, E., Swami, A.: Security issues for distributed fusion in coalition environments. In: 2018 21st International Conference on Information Fusion (FUSION), pp. 830–837. IEEE (2018)

    Google Scholar 

  6. Pham, T., Cirincione, G., Swami, A., Pearson, G., Williams, C.: Distributed analytics and information science. In: IEEE 8th International Conference on Information Fusion (2015)

    Google Scholar 

  7. Roberts, D., Lock, G., Verma, D.: Holistan: a futuristic scenario for international coalition operations. In: IEEE International Conference on Integration of Knowledge Intensive Multi-agent Systems (2007)

    Google Scholar 

  8. Pearson, G., Verma, D., de Mel, G.: Value of information: quantification and application to sensor fusion policies. In: 2018 Policies for Autonomic Data Governance PADG-2018 (2018)

    Google Scholar 

  9. Bertino, E., Verma, D., Calo, S.: A policy system for control of data fusion processes and derived data. In: 2018 21st International Conference on Information Fusion (FUSION), pp. 807–813. IEEE (2018)

    Google Scholar 

  10. Anderson, A.: A comparison of two privacy policy languages: EPAL and XACML (2005)

    Google Scholar 

  11. Agrawal, D., Calo, S., Lee, K.W., Lobo, J.: Issues in designing a policy language for distributed management of it infrastructures. In: 10th IFIP/IEEE International Symposium on Integrated Network Management, IM 2007, pp. 30–39. IEEE (2007)

    Google Scholar 

  12. Han, W., Lei, C.: A survey on policy languages in network and security management. Comput. Netw. 56(1), 477–489 (2012)

    CrossRef  Google Scholar 

  13. Bertino, E., Calo, S., Toma, M., Verma, D., Williams, C., Rivera, B.: A cognitive policy framework for next-generation distributed federated systems: concepts and research directions. In: 2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS), pp. 1876–1886. IEEE (2017)

    Google Scholar 

  14. Klein, M.: XML, RDF, and relatives. IEEE Intell. Syst. 16(2), 26–28 (2001)

    CrossRef  Google Scholar 

  15. Papernot, N., McDaniel, P., Wu, X., Jha, S., Swami, A.: Distillation as a defense to adversarial perturbations against deep neural networks. In: 2016 IEEE Symposium on Security and Privacy (SP), pp. 582–597. IEEE (2016)

    Google Scholar 

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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.

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Correspondence to Seraphin Calo .

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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.

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