Abstract: A secured virtual container is enabled to securely store personal data corresponding to a user, where such data is inaccessible to processes running outside the secured virtual container. The secured virtual container may also include an execution environment for a machine learning model where the model is securely stored and inaccessible. Personal data may be feature engineered and provided to the machine learning model for training purposes and/or to generate inference values corresponding to the user data. Inference values may thereafter be relayed by a broker application from the secured virtual container to applications external to the container. Applications may perform hyper-personalization operations based at least in part on received inference values. The broker application may enable external applications to subscribe to notifications regarding availability of inference values. The broker may also provide inference values in response to a query.

Abstract: A secured virtual container is enabled to securely store personal data corresponding to a user, where such data is inaccessible to processes running outside the secured virtual container. The secured virtual container may also include an execution environment for a machine learning model where the model is securely stored and inaccessible. Personal data may be feature engineered and provided to the machine learning model for training purposes and/or to generate inference values corresponding to the user data. Inference values may thereafter be relayed by a broker application from the secured virtual container to applications external to the container. Applications may perform hyper-personalization operations based at least in part on received inference values. The broker application may enable external applications to subscribe to notifications regarding availability of inference values. The broker may also provide inference values in response to a query.

Abstract: A secured virtual container is enabled to securely store personal data corresponding to a user, where such data is inaccessible to processes running outside the secured virtual container. The secured virtual container may also include an execution environment for a machine learning model where the model is securely stored and inaccessible. Personal data may be feature engineered and provided to the machine learning model for training purposes and/or to generate inference values corresponding to the user data. Inference values may thereafter be relayed by a broker application from the secured virtual container to applications external to the container. Applications may perform hyper-personalization operations based at least in part on received inference values. The broker application may enable external applications to subscribe to notifications regarding availability of inference values. The broker may also provide inference values in response to a query.

Abstract: A population of devices provides telemetry data and receives software changes or updates. Event buckets for respective events are found. Event buckets have counts of event instances, where each event instance is an occurrence of a corresponding event reported as telemetry by a device. Records of the software changes are provided, each change record representing a software change on a corresponding device. The event buckets are analyzed to identify which indicate an anomaly. Based on the change records and the identified event buckets, correlations between the software changes and the identified event buckets are found.

Abstract: A population of devices provides telemetry data and receives software changes or updates. Event buckets for respective events are found. Event buckets have counts of event instances, where each event instance is an occurrence of a corresponding event reported as telemetry by a device. Records of the software changes are provided, each change record representing a software change on a corresponding device. The event buckets are analyzed to identify which indicate an anomaly. Based on the change records and the identified event buckets, correlations between the software changes and the identified event buckets are found.

Abstract: A population of devices provides telemetry data and receives software changes or updates. Event buckets for respective events are found. Event buckets have counts of event instances, where each event instance is an occurrence of a corresponding event reported as telemetry by a device. Records of the software changes are provided, each change record representing a software change on a corresponding device. The event buckets are analyzed to identify which indicate an anomaly. Based on the change records and the identified event buckets, correlations between the software changes and the identified event buckets are found.

Abstract: A computing device analyzes digital certificates received from various different sites (e.g., accessed via the Internet or other network) in order to automatically detect fraudulent digital certificates. The computing device maintains a record of the digital certificates it receives from these various different sites. A certificate screening service operating remotely from the computing device also accesses these various different sites and maintains a record of the digital certificates that the service receives from these sites. In response to a request to access a target site the computing device receives a current digital certificate from the target site. The computing device determines whether the current digital certificate is genuine or fraudulent based on one or more of previously received digital certificates for the target site, confirmation certificates received from the certificate screening service, and additional characteristics of the digital certificates and/or the target site.

Abstract: A computing device analyzes digital certificates received from various different sites (e.g., accessed via the Internet or other network) in order to automatically detect fraudulent digital certificates. The computing device maintains a record of the digital certificates it receives from these various different sites. A certificate screening service operating remotely from the computing device also accesses these various different sites and maintains a record of the digital certificates that the service receives from these sites. In response to a request to access a target site the computing device receives a current digital certificate from the target site. The computing device determines whether the current digital certificate is genuine or fraudulent based on one or more of previously received digital certificates for the target site, confirmation certificates received from the certificate screening service, and additional characteristics of the digital certificates and/or the target site.