Abstract: Disclosed are methods, systems, devices, media, circuits, and other implementations, including a method that includes generating for a code block of a process executing on a controller-based device one or more code block copies defined in a virtual address space of the controller-based device, with the code block of the process being stored in a particular segment of a physical address space of the controller-based device, and with the code block configured to separately map to each of the one or more of the code block copies in the virtual address space. The method further includes processing at least a portion of one of the one or more code block copies defined in the virtual address space when the corresponding code block of the process is to be processed.

Abstract: Described herein are techniques for preventing software applications from gaining access to unauthorized biometric data in accordance with user preferences. In some embodiments, a software application requests access to sensor data collected by a sensor installed on a user device via a gateway application installed on the user device. Upon receipt of the request, the gateway application determines what types of biometric data the software application is authorized to obtain within the sensor data. The gateway application then identifies biometric data that is present within the sensor data. The sensor data is then altered such that biometric data that the software application is not authorized to obtain is obfuscated. Once the sensor data has been altered, the software application is provided access to that altered sensor data.

Abstract: Securely re-encrypting homomorphically encrypted data by receiving fully homomorphically encrypted (FHE) information from a client device, training a machine learning model using the FHE information, yielding FHE ciphertexts, applying a first transform to the FHE ciphertexts, yielding obfuscated FHE ciphertexts, sending the obfuscated FHE ciphertexts to a secure device, receiving a re-encrypted version of the obfuscated FHE ciphertexts from the secure device, applying a second transform to the re-encrypted version of the obfuscated FHE ciphertexts yielding de-obfuscated re-encrypted FHE ciphertexts, determining FHE ML model parameters according to the de-obfuscated re-encrypted ciphertexts, and sending the FHE ML model parameters to the client device.

Abstract: An artificial intelligence calculation semiconductor device is provided. The artificial intelligence calculation semiconductor device comprising: a control unit; and a MAC (Multiply and Accumulator) calculator which executes a homomorphic encryption calculation through the control unit, wherein the MAC calculator includes an NTT (Numeric Theoretic Transform)/INTT (Inverse NTT) circuit which generates cipher texts by performing a homomorphic multiplication calculation through transformation or inverse transformation of data, a cipher text multiplier which executes a multiplication calculation between the cipher texts, a cipher text adder/subtractor which executes addition and/or subtraction calculations between the cipher texts, and a rotator which performs a cyclic shift of a slot of the cipher texts.

Abstract: Systems and methods are provided for the classification of identified security vulnerabilities in software applications, and their triage based on automated decision-tree triage and/or machine learning. The disclosed system may generate a report listing detected potential vulnerability issues, and automatically determine whether the potential vulnerability issues are exploitable using automated triage policies containing decision trees or by extracting vulnerability features from the report and processing the extracted vulnerability features using machine learning models.

Abstract: An anonymous authentication service for an invulnerable secret key authentication and encryption token distribution service. Applications place a small code segment within their communications protocol, thereby allowing network participants the full benefit of perfectly secure authenticated and encrypted message traffic without concern for third party key management. This is the world's first participant-managed, independent-trust secure messaging key distribution capability.

Abstract: The present techniques may provide improved processing and functionality of performance of the 128-bit AES Algorithm, which may provide improved power consumption. For example, in an embodiment, an encryption and decryption apparatus may comprise memory storing a current state matrix of an encryption or decryption process and a plurality of multiplexers configured to receive from the memory current elements of the state matrix stored in the memory, perform a cyclic shift on the received elements of the state matrix, and transmit the shifted elements to the memory for storage as a new state matrix.

Abstract: A data analytics system is disclosed that can include a data repository configured to store data for multiple clients, a metadata repository separate from the data store, an access control system, and a policy store. The data analytics system can automatically generate metadata for data in the data repository using a metadata engine, the metadata including technical metadata and usage metadata, and store the metadata in the metadata repository. The data analytics system can obtain a client policy governing access to the data. The data analytics system can receive a request to provide the data, the request including instructions to create a pipeline to provide the data. The data analytics system can authorize, by the access control system, the request using the policy and usage metadata; create the pipeline using the technical metadata; and provide the data using the pipeline.

Abstract: Artificial intelligence (“AI”) apparatus and methods are provided for hardening security of software applications. Under the conventional approaches, additional manual investment implementing security policies does not yield proportional increases in combating cyber security threats. Using manual approaches, it is increasingly difficult to consistently apply multiple policies covering different software applications or versions. This results in increased risk and technical debt. Over time, these undesirable consequences exacerbate the likelihood of inadvertently introducing an adverse policy omission or change. As the scale of software application deployed across and organization increases, it becomes even more difficult to ensure that security policies tracked and consistently applied. This may result in ineffective, contradictory or duplicative configuration requirements.

Abstract: A data processing system includes a rich execution environment, a hardware accelerator, a trusted execution environment, and a memory. The REE includes a processor configured to execute an application. A compute kernel is executed on the hardware accelerator and the compute kernel performs computations for the application. The TEE provides relatively higher security than the REE and includes an accelerator controller for controlling operation of the hardware accelerator. The memory has an unsecure portion coupled to the REE and to the TEE, and a secure portion coupled to only the TEE. The secure portion is relatively more secure than the unsecure portion. Data that is to be accessed and used by the hardware accelerator is stored in the secure portion of the memory. In another embodiment, a method is provided for securely executing an application is the data processing system.

Abstract: Aspects of the disclosure relate to using machine learning to modify account privacy settings. A computing platform may identify initial account settings for an individual of a plurality of individuals. Using a settings optimization model, the computing platform may identify account data and third party data for the individual. Using the settings optimization model, the computing platform may identify discrepancies between the initial account settings for the first individual and account settings for a subset of the plurality of individuals having common characteristics with the individual. Based on the discrepancies, the computing platform may identify settings modifications for the individual, and may determine that a modification of the settings modifications applies to a detected interaction of the individual. The computing platform may direct an enterprise data source to perform the modification, which may cause the enterprise data source to modify the initial account settings based on the modification.

Abstract: A device may receive real time telecommunications data associated with a telecommunications network, and may select a first machine learning model from a first plurality of machine learning models based on the real time telecommunications data. The device may select a second machine learning model from a second plurality of machine learning models based on the real time telecommunications data. The device may process, in a first stage, the real time telecommunications data, with the first machine learning model, to determine a probability score indicating whether a customer technical problem telephone call will occur. The device may process, in a second stage and based on the probability score satisfying a threshold, the real time telecommunications data, with the second machine learning model, to determine a category associated with the customer technical problem telephone call, and may perform one or more actions based on the category.

Abstract: Implementations include providing a security rating and a data criticality value of one or more transactions, the one or more transactions to be recorded to a blockchain, and the blockchain being of a blockchain network, selecting a consensus protocol, the consensus protocol selected from a set of consensus protocols, and the consensus protocol selected based on the security rating and the data criticality value, defining a set of consensus nodes, the set of consensus nodes including nodes from one of a super node pool and a weak node pool, and executing, by the set of consensus nodes, the consensus protocol to record the one or more transactions to the blockchain.