Abstract: A paper tray access system for a printer includes a locking cover of paper tray, an authentication device, a weight sensor located beneath the paper tray, a printer display, a computing device including electrical circuitry, a memory storing program instructions and at least one processor. The computing device is configured to receive user input from the authentication device and determine whether the user input matches the authentication record. When user input matches authentication record, the computing device presents a selection on printer display to access a paper tray, receives the selection, records a first weight of the paper tray upon receiving the selection, unlocks the locking cover, records a second weight of the paper tray, calculates an amount of papers removed from paper tray when second weight is less than first weight, and calculates an amount of papers added to paper tray when second weight is greater than first weight.

Abstract: Techniques for providing multi-access edge computing (MEC) services security in mobile networks (e.g., service provider networks for mobile subscribers, such as for 5G networks) by parsing Application Programming Interfaces (APIs) are disclosed. In some embodiments, a system/process/computer program product for MEC services security in mobile networks by parsing APIs in accordance with some embodiments includes monitoring network traffic on a mobile network at a security platform to identify an API message associated with a new session, wherein the mobile network includes a 5G network or a converged 5G network that includes a multi-access edge computing (MEC) service; extracting mobile network identifier information from the API message at the security platform; and determining a security policy to apply at the security platform to the new session based on the mobile network identifier information.

Abstract: In an example, a collection of compute resources corresponding to a class of devices is identified with a request and a synthesis routine is performed via a number of destinations corresponding to the class of devices. In an example, a type of compute resource to use with an operation of a request is determined, a device with the type of compute resource determined for the operation of the request is identified; the identified device performs the operation of the request within a bounds of a policy, and a result of the operation is communicated to a remote storage location.

Abstract: A system performs digital notarization using a biometric identification service. A signature requesting service receives a request to validate a digital item with a signature for a person. The signature requesting service provides a payload that identifies the digital item and/or the person to an identity service. The identity service obtains one or more digital representations of biometrics for the person, determines an identity for the person, and returns a data structure including the payload and one or more identity attestations regarding the determined identity. The identity service encrypts at least a portion of the data structure using a private encryption key. A public encryption key for the identity service can then be used to decrypt the portion to verify that the data structure was generated by the identity service after determining the identity. In this way, validation can be verified to the full trust level of the identification service.

Abstract: Systems, methods, and computer-readable storage media for ensuring electronic communications have not been intercepted and manipulated. An exemplary device generates a public/private pair of keys, and transmits the public key to another device with information about the data to be shared. The second device encrypts associated data, while also executing a hash function on at least a portion of the data. The first device receives the encrypted data, decrypts it, and verifies its accuracy using a third party. The third party also executes the hash function on the data received from the first device, and transmits the output of that hash function to the first device. Both the first device and second devices and display the hash values, allowing users to visually determine if the data has been manipulated during the transaction.

Abstract: Aspects of the disclosure relate to preventing unauthorized screen capture activity. A computing platform may detect, via an infrared sensor associated with a computing device, an infrared signal from a second device attempting an unauthorized image capture of contents being displayed by a display device of the computing device. Subsequently, the computing platform may determine, via the computing platform, the contents being displayed by the display device. Then, the computing platform may retrieve a record of the contents being displayed by the display device. Then, the computing platform may determine a risk level associated with the infrared signal. Subsequently, the computing platform may perform, via the computing platform and based on the risk level, a remediation task to prevent the unauthorized image capture.

Abstract: The present disclosure relates to a method for accessing a database stored on a server using a relation. The server is coupled to a client computer via a network, wherein the relation comprises first data items, the first data items forming a partially ordered set in the first relation, wherein for each first data item a referential connection exists in the database assigning said first data item to at least one second data item of the database. The method comprises: identifying first data items of the relation referencing N second data items; for each identified first data item modifying, using a same modification method, the identified first data item M times, wherein M?N, for obtaining M unique modified data items; associating with each of modified first data items of a given first data item a respective portion of the N referential connections of the given first data item; inserting the modified first data items in the relation, thereby replacing the identified first data items.

Abstract: Aspects of the disclosure relate to preventing unauthorized screen capture activity. A computing platform may detect, via an infrared sensor associated with a computing device, an infrared signal from a second device attempting an unauthorized image capture of contents being displayed by a display device of the computing device. Subsequently, the computing platform may determine, via the computing platform, the contents being displayed by the display device. Then, the computing platform may retrieve a record of the contents being displayed by the display device. Then, the computing platform may determine a risk level associated with the infrared signal. Subsequently, the computing platform may perform, via the computing platform and based on the risk level, a remediation task to prevent the unauthorized image capture.

Abstract: Aspects of the disclosure relate to preventing unauthorized screen capture activity. A computing platform may detect, via an infrared sensor associated with a computing device, an infrared signal from a second device attempting an unauthorized image capture of contents being displayed by a display device of the computing device. Subsequently, the computing platform may determine, via the computing platform, the contents being displayed by the display device. Then, the computing platform may retrieve a record of the contents being displayed by the display device. Then, the computing platform may determine a risk level associated with the infrared signal. Subsequently, the computing platform may perform, via the computing platform and based on the risk level, a remediation task to prevent the unauthorized image capture.

Abstract: A device may obtain registration data associated with a registration of an individual. The registration data may include an image that depicts a physical key and a reference object. The device may process the image to identify a first feature of the physical key and a first measurement of the first feature based on the size of the reference object. The device may store first feature data based on the first feature and the first measurement. The device may obtain second feature data based on a second feature of the physical key and a second measurement of the second feature identified from an insertion of the physical key into a keyhole of an authentication mechanism. The device may determine whether the first feature data corresponds to the second feature data. The device may authenticate the individual based on determining that the first feature data corresponds to the second feature data.

Abstract: Methods, systems, and devices for public key protection techniques are described. An embedded multimedia card (eMMC) may be formatted to include a permanent write protect group that is configured to prevent disabling of write protection for data stored in the permanent write protect group. The eMMC may store a public key associated with a first host device in the permanent write protect group of the eMMC. A data package may be received from the host device and authenticated by using the public key stored in the permanent write protect group. The embedded memory controller may be configured to prevent modifying or writing data to a permanent write protect group.

Abstract: A software defined data security level method, computer program product, and data processing system. One embodiment may comprise intercepting, by a processor at a data security layer, an input/output (IO) request from a local software application, wherein the IO request includes a header and a data payload, analyzing, by the processor at the data security layer, the data payload of the IO request relative to a service level agreement (SLA), assigning, by the processor at the data security layer, a security level to the IO request based on the analysis.

Abstract: The present application relates to networking technologies, communication cube technologies, and, more particularly, to methods, apparatus, techniques, and means for communication security, encryption, and privacy in network communications.

Abstract: An approach includes receiving a machine learning processing job, executing the machine learning processing job using parallel processing of multiple output pixels each cycle by walking data across processing elements with broadcast weights within regions and executing parallel multiplication operations, and generating an output indicating whether the machine learning processing job was successful or failed. In some embodiments, a schedule of actions is generated for respective machine learning processing jobs. The schedule of actions may include any of a plurality of shift operations in a many to many arrangement or a one to many arrangement, shifting data across region boundaries, fetching data and weights from a memory and distribution thereof to a plurality of regions (e.g., weights are distributed to respective weight memories which subsequently broadcasts those weights in a specified order based on a schedule of actions, and where data is distributed to respective processing elements).

Abstract: The present disclosure provides for a system ensuring the integrity of received data. The system includes a processor, a trusted platform module, and a memory storing instructions. Upon a request from the processor, the trusted platform module generates an asymmetric key pair including a private key and a public key. The trusted platform module provides the public key and an encrypted private key to the processor. The processor generates a checksum of received content data and sends the checksum to the trusted platform module. The processor also loads the encrypted private key into the trusted platform module. The trusted platform module decrypts the encrypted private key, encrypts the checksum with the private key, and provides the encrypted checksum to the processor. The processor sends the content data together with the encrypted checksum to an external device. The external device may decrypt the encrypted checksum with the public key.

Abstract: The innovation disclosed and claimed herein, in one aspect thereof, comprises systems and methods of automatic classification and modeling. The innovation can include determining a failure history of networked architecture, the failure history including data immediately prior to failure. The innovation can include machine learning the failure history to determine failure indicators. The innovation can include generating a black hole model based on the failure history and the machine learning. The innovation can include monitoring a networked architecture. The networked architecture has a set of elements comprising software elements and hardware elements interconnected in a common environment. Each element of the set of elements is monitored. The innovation can include determining an element is trending towards a failure. The trend is determined by a black hole model. The innovation can include enabling security features to prevent the element from failure.

Abstract: The present disclosure provides methods and systems for secure logon. One or more method includes: determining, via authentication information provided by a user of an electronic device, that the user is authorized to access an online account provided by the online account provider; providing the user with a selectable option to enable an expedited logon process by which the user can access the online account by solely providing a particular authentication item of the user; receiving a verification credential in response to a next logon attempt using the expedited logon process; and verifying that the received verification credential matches an assigned verification credential provided to the user for use in conjunction with the next logon attempt using the expedited logon process.

Abstract: A method is disclosed of a secure component (SC) of a local attestation server (LAS) for populating an enclave associated with the LAS. The SC comprises stored encrypted population information previously received in a data packet and encrypted by an encryption key matching a private decryption key of a trusted platform module (TPM) associated with the LAS. The method comprises receiving a public part of a temporary asymmetric key from the enclave, establishing a secure session between the SC and the TPM, sending (to the TPM by using the secure session) the encrypted population information and the public part of the temporary asymmetric key, receiving (from the TPM by using the secure session) the population information decrypted by the private decryption key and re-encrypted by the public part of the temporary asymmetric key, and transmitting the re-encrypted population information to the enclave. An additional method is also disclosed for enabling the population of the enclave.

Abstract: The invention relates to methods for implementing a unified framework for big data usage analytics and security profiling across heterogeneous platforms and multiple clusters. An embodiment of the present invention may be applied in various applications and use cases. This may include auditing for Big Data lakes; capacity assessment and charge back functionality for Big Data infrastructure and clusters and resource usage analytics.

Abstract: Methods, systems and computer program products are described to improve machine learning (ML) model-based classification of data items by identifying and removing inaccurate training data. Inaccurate training samples may be identified, for example, based on excessive variance in vector space between a training sample and a mean of category training samples, and based on a variance between an assigned category and a predicted category for a training sample. Suspect or erroneous samples may be selectively removed based on, for example, vector space variance and/or prediction confidence level. As a result, ML model accuracy may be improved by training on a more accurate revised training set. ML model accuracy may (e.g., also) be improved, for example, by identifying and removing suspect categories with excessive (e.g., weighted) vector space variance. Suspect categories may be retained or revised. Users may (e.g., also) specify a prediction confidence level and/or coverage (e.g., to control accuracy).