Enabling the Autonomic Management of Federated Identity Providers

The autonomic management of federated authorization infrastructures (federations) is seen as a means for improving the monitoring and use of a service provider’s resources. However, federations are comprised of independent management domains with varying scopes of control and data ownership. The focus of this paper is on the autonomic management of federated identity providers by service providers located in other domains, when the identity providers have been diagnosed as the source of abuse. In particular, we describe how an autonomic controller, external to the domain of the identity provider, exercises control over the issuing of privilege attributes. The paper presents a conceptual design and implementation of an effector for an identity provider that is capable of enabling cross-domain autonomic management. The implementation of an effector for a SimpleSAMLphp identity provider is evaluated by demonstrating how an autonomic controller, together with the effector, is capable of responding to malicious abuse

Federated AI for building AI Solutions across Multiple Agencies

The different sets of regulations existing for differ-ent agencies within the government make the task of creating AI enabled solutions in government dif-ficult. Regulatory restrictions inhibit sharing of da-ta across different agencies, which could be a significant impediment to training AI models. We discuss the challenges that exist in environments where data cannot be freely shared and assess tech-nologies which can be used to work around these challenges. We present results on building AI models using the concept of federated AI, which al-lows creation of models without moving the training data around.Comment: Presented at AAAI FSS-18: Artificial Intelligence in Government and Public Sector, Arlington, Virginia, US