Abstract: An identity verification system receives a candidate signal transmitted from a computing device located near a secured asset in response to a target user requesting access to the secure asset. The identity verification system compares the candidate signal to a cluster of signals measured during past successful authentications by the computing device to determine a modulation factor and determines a match probability for the target user based on the determined modulation factor. The identity verification system grants the requesting target user access to the secured asset in response to determining that the match probability is greater than the operational security threshold.

Abstract: An intention detection mechanism detects a target user accessing an operational context and accesses sensor data collected by a plurality of sensors encoding signals describing one or more characteristics of the target user and surroundings of the target user. The intention detection mechanism converts the accessed sensor data into a multi-channel sensor data time series, where each channel represents a characteristic of the target user. The intention detection mechanism compares the sensor data time series to a multi-channel intentionality template to determine whether the sensor data time series contains the multi-channel intentionality template. The multi-channel intentionality template describes characteristics indicative of an intent of a target user accessing the operational context. If the sensor data time series contains the multi-channel intentionality template, the intention detection mechanism communicates a signal confirming that the target user intended to access to the operational context.

Abstract: An identity verification system receives a candidate signal transmitted from a computing device located near a secured asset in response to a target user requesting access to the secure asset. The identity verification system compares the candidate signal to a cluster of signals measured during past successful authentications by the computing device to determine a modulation factor and determines a match probability for the target user based on the determined modulation factor. The identity verification system grants the requesting target user access to the secured asset in response to determining that the match probability is greater than the operational security threshold.

Abstract: An authentication server receives a request to access an authentication server from a first device. The authentication server receives one or more proximity signals transmitted from the first device and one or more proximity signals transmitted from a second device. The second device has a trust relationship with the authentication server. The authentication server confirms that the first device and the second device are in proximity to each other based on the one or more proximity signals transmitted from the first device and second device. The authentication server verifies the trust relationship with the second device by decrypting a signed message received from the second device after confirmation that the first device and the second device are in proximity to each other. The authentication server updates a trust relationship between the first device and the server based on the verified trust relationship between the second device and the server.

Abstract: An identity verification system receives context signals from an authenticating computing device in response to a target user requesting access to a secure asset. The identity verification system identifies candidate locations for the operational context assigned to historical context signals labeled as being measured at a known location and compares the context signal to each historical signal to determine a location of the operational context corresponding to the received context signal. The identity verification system determines a match probability for the target user based on a risk score assigned to the location of the operational received context signal and grants the requesting target user access to the secured asset in response to determining that the match probability is greater than the operational security threshold.

Abstract: An identity verification system receives context signals from an authenticating computing device in response to a target user requesting access to a secure asset. The identity verification system identifies candidate locations for the operational context assigned to historical context signals labeled as being measured at a known location and compares the context signal to each historical signal to determine a location of the operational context corresponding to the received context signal. The identity verification system determines a match probability for the target user based on a risk score assigned to the location of the operational received context signal and grants the requesting target user access to the secured asset in response to determining that the match probability is greater than the operational security threshold.

Abstract: A system is disclosed for identifying a user based on the classification of user characteristic data. An identity verification system receives a request from a requesting target user for access to an operational context and characteristic data describing actions of the requesting target user. The identity verification system inputs the characteristic data to an identity confidence model to determine an identity confidence value describes a likelihood that an identity of the requesting target user matches an authenticating identity and determines a false match rate and false non-match rate, which represent a performance of the identity confidence model. The identity verification system determines a match probability for the requesting target user by adjusting the identity confidence value based on the determined false match rate and false non-match rate and grants the requesting target user access to the operational context if match probability is greater than the operational security threshold.

Abstract: A system and method are disclosed for identifying a user based on the classification of user movement data. An identity verification system receives a sequence of motion data characterizing movements performed by a target user. The sequence of motion data is received as a point cloud of the motion data. The point cloud is input a machine-learned model trained based on manually labeled point clusters of a training set of motion data that each represent a movement. The machine-learned model identifies a movement represented by the point cloud of the motion data and assigns a label describing the movement the point cloud. The system generates a labeled representation of the sequence of motion data comprising the label identifying a portion of the sequence of motion data corresponding to the identified movement.

Abstract: A system and method are disclosed for identifying a user based on the classification of user movement data. An identity verification system receives a sequence of motion data from a mobile device operated by a target user. From the sequences of motion data, the identity verification system identifies a plurality of identity blocks representing different movements performed by the target user and encodes a set of signature sequences from each identity block into a feature vector. Each feature vector is input to a confidence model to output an identity confidence value for an identity block. An identity confidence value describes a confidence that the movement in the identity block was performed by the target user. The identity confidence value is compared to an operational security threshold and if identity confidence value is above the threshold, the target user is granted access to an operational context.

Abstract: A system is disclosed for identifying a user based on the classification of user characteristic data. An identity verification system receives a request from a requesting target user for access to an operational context and characteristic data describing actions of the requesting target user. The identity verification system inputs the characteristic data to an identity confidence model to determine an identity confidence value describes a likelihood that an identity of the requesting target user matches an authenticating identity and determines a false match rate and false non-match rate, which represent a performance of the identity confidence model. The identity verification system determines a match probability for the requesting target user by adjusting the identity confidence value based on the determined false match rate and false non-match rate and grants the requesting target user access to the operational context if match probability is greater than the operational security threshold.

Abstract: An intention detection mechanism detects a target user accessing an operational context and accesses sensor data collected by a plurality of sensors encoding signals describing one or more characteristics of the target user and surroundings of the target user. The intention detection mechanism converts the accessed sensor data into a multi-channel sensor data time series, where each channel represents a characteristic of the target user. The intention detection mechanism compares the sensor data time series to a multi-channel intentionality template to determine whether the sensor data time series contains the multi-channel intentionality template. The multi-channel intentionality template describes characteristics indicative of an intent of a target user accessing the operational context. If the sensor data time series contains the multi-channel intentionality template, the intention detection mechanism communicates a signal confirming that the target user intended to access to the operational context.

Abstract: A system and method are disclosed for identifying a user based on the classification of user movement data. An identity verification system receives a sequence of motion data from a mobile device operated by a target user. From the sequences of motion data, the identity verification system identifies a plurality of identity blocks representing different movements performed by the target user and encodes a set of signature sequences from each identity block into a feature vector. Each feature vector is input to a confidence model to output an identity confidence value for an identity block. An identity confidence value describes a confidence that the movement in the identity block was performed by the target user. The identity confidence value is compared to an operational security threshold and if identity confidence value is above the threshold, the target user is granted access to an operational context.

Abstract: An intention detection mechanism detects a target user accessing an operational context and accesses sensor data collected by a plurality of sensors encoding signals describing one or more characteristics of the target user and surroundings of the target user. The intention detection mechanism converts the accessed sensor data into a multi-channel sensor data time series, where each channel represents a characteristic of the target user. The intention detection mechanism compares the sensor data time series to a multi-channel intentionality template to determine whether the sensor data time series contains the multi-channel intentionality template. The multi-channel intentionality template describes characteristics indicative of an intent of a target user accessing the operational context. If the sensor data time series contains the multi-channel intentionality template, the intention detection mechanism communicates a signal confirming that the target user intended to access to the operational context.

Abstract: A system and method are disclosed for identifying a user based on the classification of user movement data. An identity verification system receives a sequence of motion data from a mobile device operated by a target user. From the sequences of motion data, the identity verification system identifies a plurality of identity blocks representing different movements performed by the target user and encodes a set of signature sequences from each identity block into a feature vector. Each feature vector is input to a confidence model to output an identity confidence value for an identity block. An identity confidence value describes a confidence that the movement in the identity block was performed by the target user. The identity confidence value is compared to an operational security threshold and if identity confidence value is above the threshold, the target user is granted access to an operational context.

Abstract: An identity verification system is disclosed for identifying a user based on the classification of user characteristic data. The identity verification system receives a request for access to an operational context that comprises authentication credentials representing an identity of a requesting target user. The system authenticates the identity of the requesting target user by determining an identity confidence value describing a likelihood that an identity of the requesting target user matches the identity represented by the authentication credentials. The system determines a proximity of the requesting target user to the operational context by determining a distance between a location of the requesting computing device and a location of an authenticating computing device operated by the requesting target user. The system grants the request from the requesting target user for access to the operational context if the requesting target user is within a threshold proximity of the operational context.

Abstract: A system and method are disclosed for identifying a user based on the classification of user movement data. An identity verification system receives a sequence of motion data from a mobile device operated by a target user. From the sequences of motion data, the identity verification system identifies a plurality of identity blocks representing different movements performed by the target user and encodes a set of signature sequences from each identity block into a feature vector. Each feature vector is input to a confidence model to output an identity confidence value for an identity block. An identity confidence value describes a confidence that the movement in the identity block was performed by the target user. The identity confidence value is compared to an operational security threshold and if identity confidence value is above the threshold, the target user is granted access to an operational context.

Abstract: A system and method are disclosed for identifying a user based on the classification of user movement data. An identity verification system receives a sequence of motion data from a mobile device operated by a target user. From the sequences of motion data, the identity verification system identifies a plurality of identity blocks representing different movements performed by the target user and encodes a set of signature sequences from each identity block into a feature vector. Each feature vector is input to a confidence model to output an identity confidence value for an identity block. An identity confidence value describes a confidence that the movement in the identity block was performed by the target user. The identity confidence value is compared to an operational security threshold and if identity confidence value is above the threshold, the target user is granted access to an operational context.

Abstract: A system and method are disclosed for identifying a user based on the classification of user movement data. An identity verification system receives a sequence of motion data characterizing movements performed by a target user. The sequence of motion data is received as a point cloud of the motion data. The point cloud is input a machine-learned model trained based on manually labeled point clusters of a training set of motion data that each represent a movement. The machine-learned model identifies a movement represented by the point cloud of the motion data and assigns a label describing the movement the point cloud. The system generates a labeled representation of the sequence of motion data comprising the label identifying a portion of the sequence of motion data corresponding to the identified movement.

Abstract: A system and method are disclosed for identifying a user based on the classification of user movement data. An identity verification system receives a sequence of motion data from a mobile device operated by a target user. From the sequences of motion data, the identity verification system identifies a plurality of identity blocks representing different movements performed by the target user and encodes a set of signature sequences from each identity block into a feature vector. Each feature vector is input to a confidence model to output an identity confidence value for an identity block. An identity confidence value describes a confidence that the movement in the identity block was performed by the target user. The identity confidence value is compared to an operational security threshold and if identity confidence value is above the threshold, the target user is granted access to an operational context.

Abstract: A system and method are disclosed for identifying a user based on the classification of user movement data. An identity verification system receives a sequence of motion data characterizing movements performed by a target user. The sequence of motion data is received as a point cloud of the motion data. The point cloud is input a machine-learned model trained based on manually labeled point clusters of a training set of motion data that each represent a movement. The machine-learned model identifies a movement represented by the point cloud of the motion data and assigns a label describing the movement the point cloud. The system generates a labeled representation of the sequence of motion data comprising the label identifying a portion of the sequence of motion data corresponding to the identified movement.