Abstract: Techniques are disclosed relating to determining risk associated with automated authentication decisions for a multi-factor authentication scheme. In disclosed embodiments, a server system sends requests corresponding to factors in a current multi-factor authentication procedure to a mobile device. The system receives, from the mobile device, automatically generated responses for the factors, where the responses are automatically generated at the mobile device using a machine learning model based on a current set of parameters for the current procedure and a previous set of parameters for a prior procedure. Based on a current state of the mobile device received with the automatically generated responses and prior states of the mobile device stored at the server computer system, the system determines a risk score for the automatically generated responses.

Abstract: Techniques are disclosed relating to automating authentication decisions for a multi-factor authentication scheme based on computer learning. In disclosed embodiments, a mobile device receives a first request corresponding to a factor in a first multi-factor authentication procedure. Based on user input approving or denying the first request, the mobile device sends a response to the first request and stores values of multiple parameters associated with the first request. The mobile device receives a second request corresponding to a factor in a second multi-factor authentication procedure where the second request is for authentication for a different account than the first request. The mobile device automatically generates an approval response to the second request based on performing a computer learning process on inputs that include values of multiple parameters for the second request and the stored values of the multiple parameters associated with the first request.

Abstract: Techniques are disclosed relating to automating authentication decisions for a multi-factor authentication scheme based on computer learning. In disclosed embodiments, a mobile device receives a first request corresponding to a factor in a first multi-factor authentication procedure. Based on user input approving or denying the first request, the mobile device sends a response to the first request and stores values of multiple parameters associated with the first request. The mobile device receives a second request corresponding to a factor in a second multi-factor authentication procedure where the second request is for authentication for a different account than the first request. The mobile device automatically generates an approval response to the second request based on performing a computer learning process on inputs that include values of multiple parameters for the second request and the stored values of the multiple parameters associated with the first request.

Abstract: Techniques are disclosed relating to automating authentication decisions for a multi-factor authentication scheme based on computer learning. In disclosed embodiments, a mobile device receives a first request corresponding to a factor in a first multi-factor authentication procedure. Based on user input approving or denying the first request, the mobile device sends a response to the first request and stores values of multiple parameters associated with the first request. The mobile device receives a second request corresponding to a factor in a second multi-factor authentication procedure where the second request is for authentication for a different account than the first request. The mobile device automatically generates an approval response to the second request based on performing a computer learning process on inputs that include values of multiple parameters for the second request and the stored values of the multiple parameters associated with the first request.

Abstract: A method and a system are presented in which federated domains interact within a federated environment. Domains within a federation can initiate federated single-sign-on operations for a user at other federated domains. A point-of-contact server within a domain relies upon a trust proxy within the domain to manage trust relationships between the domain and the federation. Trust proxies interpret assertions from other federated domains as necessary. Trust proxies may have a trust relationship with one or more trust brokers, and a trust proxy may rely upon a trust broker for assistance in interpreting assertions. When a user is provisioned at a particular federated domain, the federated domain can provision the user to other federated domains within the federated environment. A provision operation may include creating or deleting an account for a user, pushing updated user account information including attributes, and requesting updates on account information including attributes.