Abstract
Machine Learning and Artificial Intelligence models are created, trained and used by different entities. The entity that curates data used for the model is frequently different from the entity that trains the model, which is different yet again from the end user of the trained model. The end user needs to trust the received AI model, and this requires having the provenance information about how the model was trained, and the data the model was trained on. This chapter describes how blockchain can be used to track the provenance of training models, leading to better trusted Artificial Intelligence.
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References
Mehri, V.A., Tutschku, K.: Flexible privacy and high trust in the next generation internet-the use case of a cloud-based marketplace for AI. In: Swedish National Computer Networking Workshop (2017)
Athalye, A., Sutskever, I.: Synthesizing robust adversarial examples. arXiv preprint arXiv:1707.07397 (2017)
Baracaldo, N., Chen, B., Ludwig, H., Safavi, J.A.: Mitigating poisoning attacks on machine learning models: a data provenance based approach. In: Proceedings of the 10th ACM Workshop on Artificial Intelligence and Security, pp. 103–110. ACM (2017)
Barreno, M., Nelson, B., Sears, R., Joseph, A.D., Tygar, J.D.: Can machine learning be secure? In: Proceedings of the 2006 ACM Symposium on Information, Computer and Communications Security, pp. 16–25. ACM (2006)
Biggio, B., Didaci, L., Fumera, G., Roli, F.: Poisoning attacks to compromise face templates. In: 2013 International Conference on Biometrics (ICB), pp. 1–7. IEEE (2013)
Biggio, B., Fumera, G., Roli, F.: Security evaluation of pattern classifiers under attack. IEEE Trans. Knowl. Data Eng. 26(4), 984–996 (2014)
Bray, T., Paoli, J., Sperberg-McQueen, C.M., Maler, E., Yergeau, F.: Extensible markup language (XML). World Wide Web J. 2(4), 27–66 (1997)
Buneman, P., Khanna, S., Wang-Chiew, T.: Why and where: a characterization of data provenance. In: Van den Bussche, J., Vianu, V. (eds.) ICDT 2001. LNCS, vol. 1973, pp. 316–330. Springer, Heidelberg (2001). https://doi.org/10.1007/3-540-44503-X_20
Calders, T., Kamiran, F.: Classification with no discrimination by preferential sampling. In: Proceedings of 19th Machine Learning Conference. Belgium and The Netherlands (2010)
Chen, A., Wu, Y., Haeberlen, A., Loo, B.T., Zhou, W.: Architecture, experiences, and the road ahead. In: CIDR, Data Provenance at Internet Scale (2017)
Cretu, G.F., Stavrou, A., Locasto, M.E., Stolfo, S.J., Keromytis, A.D.: Casting out demons: sanitizing training data for anomaly sensors. In: IEEE Symposium on Security and Privacy, SP 2008, pp. 81–95. IEEE (2008)
Fowler, M., Kobryn, C., Scott, K.: UML Distilled: A Brief Guide to the Standard Object Modeling Language. Addison-Wesley Professional, Boston (2004)
Huang, L., Joseph, A.D., Nelson, B., Rubinstein, B.I., Tygar, J.D.: Adversarial machine learning. In Proceedings of the 4th ACM Workshop on Security and Artificial Intelligence, pp. 43–58. ACM (2011)
Jabal, A.A., Bertino, E.: SimP: secure interoperable multi-granular provenance framework. In: 2016 IEEE 12th International Conference on e-Science (e-Science), pp. 270–275. IEEE (2016)
Li, B., Vorobeychik, Y.: Feature cross-substitution in adversarial classification. In: Advances in Neural Information Processing Systems, pp. 2087–2095 (2014)
Liang, X., Shetty, S., Tosh, D., Kamhoua, C., Kwiat, K., Njilla, L.: Provchain: a blockchain-based data provenance architecture in cloud environment with enhanced privacy and availability. In: Proceedings of the 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, pp. 468–477. IEEE Press (2017)
Loo, B.T., et al.: Declarative networking: language, execution and optimization. In: Proceedings of the 2006 ACM SIGMOD International Conference on Management of Data, pp. 97–108. ACM (2006)
Ma, S., et al.: LAMP: data provenance for graph based machine learning algorithms through derivative computation. In Proceedings of the 2017 11th Joint Meeting on Foundations of Software Engineering, pp. 786–797. ACM (2017)
McDaniel, P.D., Butler, K.R.B., McLaughlin, S.E., Sion, R., Zadok, E., Winslett, M.: Towards a Secure and efficient system for end-to-end provenance. In: TaPP (2010)
McMahan, H.B., Moore, E., Ramage, D., Hampson, S., et al.: Communication-efficient learning of deep networks from decentralized data. arXiv preprint arXiv:1602.05629 (2016)
Pham, T., Solomon, L., Ciricionne, G., Henz, B.: Prevailing in a complex world: ARL’s essential research area on AI & ML. NATO IST-160 (2018)
Ramachandran, A., Kantarcioglu, M.: SmartProvenance: a distributed, blockchain based dataprovenance system. In: Proceedings of the Eighth ACM Conference on Data and Application Security and Privacy, pp. 35–42. ACM (2018)
Satyanarayanan, M.: The emergence of edge computing. Computer 50(1), 30–39 (2017)
Schelter, S., Böse, J.-H., Kirschnick, J., Klein, T., Seufert, S.: Automatically tracking metadata and provenance of machine learning experiments. In: Machine Learning Systems Workshop at NIPS (2017)
Verma, D., Cirincione, G., Pham, T., Ko, B.J.: Generation and management of training data for AI-based algorithms targeted at coalition operations. In Ground/Air Multisensor Interoperability, Integration, and Networking for Persistent ISR IX, vol. 10635, p. 106350U. International Society for Optics and Photonics (2018)
Verma, D., Julier, S., Cirincione, G.: Federated AI for building AI solutions across multiple agencies. arXiv preprint arXiv:1809.10036 (2018)
Verma, D.C., Bent, G.: Policy enabled caching for distributed AI. In: 2017 IEEE International Conference on Big Data (Big Data), pp. 3017–3023. IEEE (2017)
Woodruff, A., Stonebraker, M.: Supporting fine-grained data lineage in a database visualization environment. In: Proceedings of 13th International Conference on Data Engineering, pp. 91–102. IEEE (1997)
Wylot, M., Cudré-Mauroux, P., Hauswirth, M., Groth, P.: Storing, tracking, and querying provenance in linked data. IEEE Trans. Knowl. Data Eng. 29(8), 1751–1764 (2017)
Xiao, H., Xiao, H., Eckert, C.: Adversarial label flips attack on support vector machines. In: ECAI, pp. 870–875 (2012)
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Sarpatwar, K. et al. (2019). Towards Enabling Trusted Artificial Intelligence via Blockchain. 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_8
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