Abstract: The present teaching relates to method, system, medium, and implementations for authenticating a user. A first request is received to set up authentication information with respect to a user, wherein the first request specifies a type of information to be used for future authentication of the user. It is determined whether the type of information related to the user poses risks based on a reverse information search result. The type of information for being used for future authentication of the user is rejected when the type of information is determined to pose risks.

Abstract: A system can include a producer device to receive reference data from a reference station. The system can include a queue device to store a reference message, corresponding to the reference data, in a message queue. The system can include a mapping device to store mapping information indicating that the message queue is associated with the reference station. The system can include a consumer device to identify the message queue as being associated with a microservice to be provided to a client device based on a microservice request. The message queue can be identified based on the mapping information. The consumer device can obtain the reference message from the message queue, generate corrections data associated with the client device, and provide the corrections data.

Abstract: A method, a device, and a non-transitory storage medium each provide an object recognition service that identifies an object within an image or a video. An object is identified according to a general classification based on a domain-based inference model, and is subsequently further identified according to a sub-classification of the general classification based on another domain-based inference model. The domain-based inference models are hierarchical. The object recognition of the object may be used in support of end user services.

Abstract: A system described herein may provide a technique for the use of machine learning techniques to perform authentication, such as biometrics-based user authentication. For example, user biometric information (e.g., facial features, fingerprints, voice, etc.) of a user may be used to train a machine learning model, in addition to a noise vector. A representation of the biometric information (e.g., an image file including a picture of the user's face, an encoded file with vectors or other representation of the user's fingerprint, a sound file including the user's voice, etc.) may be iteratively transformed until the transformed biometric information matches the noise vector, and the machine learning model may be trained based on the set of transformations that ultimately yield the noise vector, when given the biometric information.

Abstract: A device may receive, from a user equipment (UE), a request to allocate one or more computing resources for an application executing on the UE. The device may be associated with an edge node of a mobile network and the UE may be within a coverage area associated with the edge node. The device may receive a remote execution file package that is associated with code to be executed using the one or more computing resources. The device may assign the one or more computing resources for the application. The device may cause at least one of installation of the code for execution by the one or more computing resources, or execution of the code using the one or more computing resources. The device may transmit a response that provides an indication that the one or more computing resources have been allocated for the application.

Abstract: A system described herein may provide a technique for the use of machine learning techniques to perform authentication, such as biometrics-based user authentication. For example, user biometric information (e.g., facial features, fingerprints, voice, etc.) of a user may be used to train a machine learning model, in addition to a noise vector. A representation of the biometric information (e.g., an image file including a picture of the user's face, an encoded file with vectors or other representation of the user's fingerprint, a sound file including the user's voice, etc.) may be iteratively transformed until the transformed biometric information matches the noise vector, and the machine learning model may be trained based on the set of transformations that ultimately yield the noise vector, when given the biometric information.

Abstract: A system described herein may provide a technique for the use of machine learning techniques to perform authentication, such as biometrics-based user authentication. For example, user biometric information (e.g., facial features, fingerprints, voice, etc.) of a user may be used to train a machine learning model, in addition to a noise vector. A representation of the biometric information (e.g., an image file including a picture of the user's face, an encoded file with vectors or other representation of the user's fingerprint, a sound file including the user's voice, etc.) may be iteratively transformed until the transformed biometric information matches the noise vector, and the machine learning model may be trained based on the set of transformations that ultimately yield the noise vector, when given the biometric information.

Abstract: Systems and methods described herein enable data analytics for secure cloud compute data that protects the integrity and confidentiality of the underlying data. A network device in a network creates an instance of a Trusted Execution Environment (TEE). The network device generates, in the TEE instance, transactional data that includes private information and removes the private information from the transactional data to generate extracted data. The network device encrypts, within the TEE instance, the extracted data and exports the encrypted extracted data to a memory outside of the TEE instance.

Abstract: A device determines, based on location verification data that has been received, that the device is indoors at a first geographic location. The device determines a base measured barometric pressure, and an initial floor that the device is located on in a structure that includes the first geographic location. The device determines an adjusted measured barometric pressure for a second geographic location based on a second measured barometric pressure for the second geographic location and one or more reference barometric pressures that are associated with a reference location. The device determines an altitude for the second geographic location based on the base measured barometric pressure and the adjusted measured barometric pressures. The device causes a server to predict a floor that the device is located on at the second geographic location and to provide floor data that identifies the floor to an interface that is accessible to the device.

Abstract: The present teaching relates to method, system, medium, and implementations for authenticating a user. An authentication session is initiated to authenticate, via iris, a person claiming to be an authorized user. A trajectory is determined with respect to a display visible to the person. A plurality of focus dots are generated with corresponding coordinates on the trajectory with respect to the display and rendered to the person on the display in accordance with the corresponding coordinates during a specified period of time. A plurality of pictures of the iris of the person are captured during the period of time and used to determine whether the person is live based on the captured plurality of pictures of the iris of the person.

Abstract: The present teaching relates to method, system, medium, and implementations for authenticating a user. A first request is received to set up authentication information with respect to a user, wherein the first request specifies a type of information to be used for future authentication of the user. It is determined whether the type of information related to the user poses risks based on a reverse information search result. The type of information for being used for future authentication of the user is rejected when the type of information is determined to pose risks.

Abstract: The present teaching relates to method, system, medium, and implementations for authenticating a user. An authentication session is initiated to authenticate, via iris, a person claiming to be an authorized user. An obfuscation configuration is retrieved with respect to the authorized user and used to determine a sub-obfuscation configuration in accordance with information associated with the authentication session. A set of expected features associated with the sub-obfuscation configuration is obtained. One or more pictures are captured from the iris of the person who is expected to carry out at least one obfuscation measure consistent with the sub-obfuscation configuration for the authorized user in accordance with the information related to the authentication session and are used to determine whether the person is live based on the set of expected features associated with the sub-obfuscation configuration.

Abstract: A system described herein may provide a technique for the use of machine learning techniques to perform authentication, such as biometrics-based user authentication. For example, user biometric information (e.g., facial features, fingerprints, voice, etc.) of a user may be used to train a machine learning model, in addition to a noise vector. A representation of the biometric information (e.g., an image file including a picture of the user's face, an encoded file with vectors or other representation of the user's fingerprint, a sound file including the user's voice, etc.) may be iteratively transformed until the transformed biometric information matches the noise vector, and the machine learning model may be trained based on the set of transformations that ultimately yield the noise vector, when given the biometric information.

Abstract: The present teaching relates to method, system, medium, and implementations for authenticating a user. A first request is received to set up authentication information with respect to a user, wherein the first request specifies a type of information to be used for future authentication of the user. It is determined whether the type of information related to the user poses risks based on a reverse information search result. The type of information for being used for future authentication of the user is rejected when the type of information is determined to pose risks.

Abstract: A system can include a producer device to receive reference data from a reference station. The system can include a queue device to store a reference message, corresponding to the reference data, in a message queue. The system can include a mapping device to store mapping information indicating that the message queue is associated with the reference station. The system can include a consumer device to identify the message queue as being associated with a microservice to be provided to a client device based on a microservice request. The message queue can be identified based on the mapping information. The consumer device can obtain the reference message from the message queue, generate corrections data associated with the client device, and provide the corrections data.

Abstract: A device determines, based on location verification data that has been received, that the device is indoors at a first geographic location. The device determines a base measured barometric pressure, and an initial floor that the device is located on in a structure that includes the first geographic location. The device determines an adjusted measured barometric pressure for a second geographic location based on a second measured barometric pressure for the second geographic location and one or more reference barometric pressures that are associated with a reference location. The device determines an altitude for the second geographic location based on the base measured barometric pressure and the adjusted measured barometric pressures. The device causes a server to predict a floor that the device is located on at the second geographic location and to provide floor data that identifies the floor to an interface that is accessible to the device.

Abstract: A device may receive, from a user equipment (UE), a request to allocate one or more computing resources for an application executing on the UE. The device may be associated with an edge node of a mobile network and the UE may be within a coverage area associated with the edge node. The device may receive a remote execution file package that is associated with code to be executed using the one or more computing resources. The device may assign the one or more computing resources for the application. The device may cause at least one of installation of the code for execution by the one or more computing resources, or execution of the code using the one or more computing resources. The device may transmit a response that provides an indication that the one or more computing resources have been allocated for the application.

Abstract: A system can include a producer device to receive reference data from a reference station. The system can include a queue device to store a reference message, corresponding to the reference data, in a message queue. The system can include a mapping device to store mapping information indicating that the message queue is associated with the reference station. The system can include a consumer device to identify the message queue as being associated with a microservice to be provided to a client device based on a microservice request. The message queue can be identified based on the mapping information. The consumer device can obtain the reference message from the message queue, generate corrections data associated with the client device, and provide the corrections data.

Abstract: A method, a device, and a non-transitory storage medium each provide an object recognition service that identifies an object within an image or a video. An object is identified according to a general classification based on a domain-based inference model, and is subsequently further identified according to a sub-classification of the general classification based on another domain-based inference model. The domain-based inference models are hierarchical. The object recognition of the object may be used in support of end user services.

Abstract: Systems and methods described herein enable data analytics for secure cloud compute data that protects the integrity and confidentiality of the underlying data. A network device in a network creates an instance of a Trusted Execution Environment (TEE). The network device generates, in the TEE instance, transactional data that includes private information and removes the private information from the transactional data to generate extracted data. The network device encrypts, within the TEE instance, the extracted data and exports the encrypted extracted data to a memory outside of the TEE instance.