Abstract: Methods and systems for securely managing personal data associated with image processing include an image sensor configured to capture an image, a local computer system local to the image sensor, and a backend computer system remote from the image sensor. The local computer system has a processor with a trusted execution environment (TEE) that detects anomalies in images from the image sensor, extracts personal data from the image, and encrypts the personal data. The local computer system then sends the extracted, encrypted personal data to the backend computer system, where a backend TEE decrypts the extracted, encrypted personal data, and performs data processing by comparing the decrypted personal data to other personal data that is stored in a backend database in the backend computer system.

Abstract: A system that includes memory and a microcontroller including an analog-to-digital converter (ADC) and in communication with the memory. The microcontroller is configured to define a fingerprint that includes a baseline measurement of side-channel traces of a side-channel retrieved from the ADC, during an enrollment period of the system, wherein the enrollment period includes measuring voltage prior to runtime operation, receive a runtime measurement from the ADC that includes voltage of at least the separate microcontroller during runtime, compare the runtime measurement to the fingerprint, and in response to the measurement exceeding a threshold, executing a countermeasure operation against software ran by the separate processor.

Abstract: A system and method are disclosed for processing data subject rights requests. The system and method advantageously enable data controllers to train machine learning models on unaltered data having PII, while maintaining the privacy of the unaltered data and enabling compliance with data subject rights requests with respect to the data. The system and method incorporate a biometric database that stores biometric data extracted from the unaltered data having PII. In order to identify data relating to a data subject rights request, biometric data is received from the data subject and is matched against the biometric data stored in the biometric database. Based on the matched biometric data, the original unaltered source data having PII can be identified for the purpose of exercising one or more data subject rights, such as erasure, access, and objection to processing.

Abstract: A method executed by an on-board unit of a vehicle includes receiving real-time data from vehicles in a convoy, wherein the data includes an indicator regarding a vehicle type within the convoy, providing a policy with a set of rules and comparing the data with the policy, providing a set of reference data corresponding to the convoy, wherein the set of reference data includes a trend of previous data indicating normal operation of the convoy, comparing the set of data with the set of reference data, wherein a determination of whether the set of data deviates from the trend of previous data is based on operation of one of the vehicles in the convoy, and initiating an operation when there is both a violation of the policy and a deviation within the set of data from the set of reference data is detected.

Abstract: An approach for managing privacy-relevant data. Disclosed embodiments significantly improve the computational efficiency of training machine-learning models while still protecting privacy-relevant data in the training data.

Abstract: Performing semantic segmentation in an absence of labels for one or more semantic classes is provided. One or more weak predictors are utilized to obtain label proposals of novel classes for an original dataset for which at least a subset of sematic classes are unlabeled classes. The label proposals are merged with ground truth of the original dataset to generate a merged dataset, the ground truth defining labeled classes of portions of the original dataset. A machine learning model is trained using the merged dataset. The machine learning model is utilized for performing semantic segmentation on image data.

Abstract: The present disclosure includes descriptions of methods and systems for executing a countermeasure against executed ransomware and for extracting an encryption key used by the ransomware. Embodiments disclosed herein comprise analyzing power consumption data of one or more processors executing the ransomware.

Abstract: A computer-implemented system and method relate to establishing a secure pairing between a first electronic control unit (ECU), which is identifiable by a first identifier, and a second ECU, which is identifiable by a second identifier. A first pairing request is received from the first ECU to pair with the second ECU. The first pairing request includes the second identifier. Session data is generated. The session data includes at least a session identifier and a master session key. A first message is transmitted to the first ECU. The first message includes the session identifier and the master session key. A second pairing request is received from the second ECU to pair with the first ECU. The second pairing request includes the session identifier and the first identifier. A second message is transmitted to the second ECU. The second message includes at least the master session key.

Abstract: Systems and methods relate to pairing a first electronic control unit (ECU) to a second ECU. First identification data of the first ECU is transmitted to the second ECU. Second identification data of the second ECU is received from the second ECU. After a request to pair, the first ECU receives session data from a server. The session data includes a session identifier (ID) to identify the pairing, a master session key (MSK), a first token, and security information of the second ECU. The first ECU derives session keys based on the MSK. The session ID and the first token's encryption first token are transmitted to the second ECU. The first ECU receives and decrypts encryption of a second token from the second ECU. Secure communication is established between the first ECU and the second ECU via the session keys after the first ECU validates the second token.

Abstract: Disclosed are systems and methods for a computerized framework that provides an improved, secure computational environment between trusted and untrusted devices (e.g., a Trusted Execution Environment (TEE) and graphics processing unit (GPU), respectively) for executing and offloading Convolutional Neural Network (CNN) computations and operations. The disclosed framework can operate to perform such secure offloading and processing not only during inference computations of the CNN, but also during training of the CNN. The disclosed framework operates to enable training and execution of CNN models, whereby the data used for such operations are held securely while they are in use, in transit (e.g., between the TEE and GPU) and while in storage.

Abstract: Methods and systems of leveraging multiple data-collection entities to protect personal data. Image data is captured via an image sensor of a vehicle. A classifier can partition the captured image data into personal data and non-personal data, and can assign a confidence score to the personal data. If the confidence score is below a threshold, the vehicle can broadcast a wireless signal to other data-collection entities such as other vehicles or road-side unit, wherein the wireless signal includes a request for the other data-collection entities to inform the vehicle if those data-collection entities have detected personal data at a time corresponding to when the image sensor of the vehicle captured the image data. If the other data-collection entities indeed detected such personal data at the time, the vehicle can perform various actions such as deleting the images, or the portions that include personal data.

Abstract: Methods and systems for securely managing personal data associated with image processing include an image sensor configured to capture an image, a local computer system local to the image sensor, and a backend computer system remote from the image sensor. The local computer system has a processor with a trusted execution environment (TEE) that detects anomalies in images from the image sensor, extracts personal data from the image, and encrypts the personal data. The local computer system then sends the extracted, encrypted personal data to the backend computer system, where a backend TEE decrypts the extracted, encrypted personal data, and performs data processing by comparing the decrypted personal data to other personal data that is stored in a backend database in the backend computer system.

Abstract: Performing semantic segmentation in an absence of labels for one or more semantic classes is provided. One or more weak predictors are utilized to obtain label proposals of novel classes for an original dataset for which at least a subset of sematic classes are unlabeled classes. The label proposals are merged with ground truth of the original dataset to generate a merged dataset, the ground truth defining labeled classes of portions of the original dataset. A machine learning model is trained using the merged dataset. The machine learning model is utilized for performing semantic segmentation on image data.

Abstract: A system includes a memory and a processor in communication with the memory. The processor is programmed to receive a runtime measurement from a sensor regarding the physical attribute of at least the separate processor during runtime; compare the runtime measurement of the physical attribute to a fingerprint that includes a baseline measurement of a physical attribute of at least a separate processor during an evaluation period of the system, and in response to the measurement exceeding a threshold, executing a countermeasure operation against software ran by the separate processor.

Abstract: A system and method are disclosed for processing data subject rights requests. The system and method advantageously enable data controllers to train machine learning models on unaltered data having PII, while maintaining the privacy of the unaltered data and enabling compliance with data subject rights requests with respect to the data. The system and method incorporate a biometric database that stores biometric data extracted from the unaltered data having PII. In order to identify data relating to a data subject rights request, biometric data is received from the data subject and is matched against the biometric data stored in the biometric database. Based on the matched biometric data, the original unaltered source data having PII can be identified for the purpose of exercising one or more data subject rights, such as erasure, access, and objection to processing.

Abstract: A system includes memory, a processor in communication with the memory. The processor is programmed to define a fingerprint that includes a baseline measurement of a physical attribute of at least a separate processor during an enrollment period of the system, wherein the enrollment period includes measuring the physical attribute of the processor prior to runtime operation, receiving a runtime measurement from a sensor regarding the physical attribute of at least the separate processor during runtime, comparing the runtime measurement of the physical attribute to the fingerprint, and outputting a multi-dimensional domain image in response to the runtime measurement.

Abstract: A vehicle system includes a first vehicle bus, wherein the first vehicle bus includes one or more electronic control units (ECUs) configured to operate, wherein the one or more ECUs are configured to communicate with a remote server, a second vehicle bus, wherein the second vehicle bus is configured to communicate to the one or more ECUs, wherein the second vehicle bus includes one or more vehicle driving ECUs configured to operate vehicle driving functionality, a gateway controller configured to control communication between the first vehicle bus and the second vehicle bus, and a honeypot configured to emulate vehicle data, wherein the honeypot is further configured to monitor activity from a remote attacker.

Abstract: Instruction classification and software intrusion detection is performed. Program instruction execution of a processor of a microcontroller unit (MCU) is monitored via side-channel signal analysis, the monitoring including capturing a signal trace of a physical property of the MCU that leaks information correlated with the program instruction execution of the MCU, the signal trace indicating a value of the physical property over time. From the signal trace, time domain features, frequency domain features, and Mel Frequency Cepstral Coefficients (MFCC) features are extracted. A model is utilized for instruction detection to identify an execution signature based on the time domain features, frequency domain features, and MFCC features. The execution signature is compared to one or more reference instruction signatures. A remedial action is performed responsive to the execution signature failing to match to the one or more reference instruction signatures.

Abstract: A system that includes memory and a microcontroller including an analog-to-digital converter (ADC) and in communication with the memory. The microcontroller is configured to define a fingerprint that includes a baseline measurement of side-channel traces of a side-channel retrieved from the ADC, during an enrollment period of the system, wherein the enrollment period includes measuring voltage prior to runtime operation, receive a runtime measurement from the ADC that includes voltage of at least the separate microcontroller during runtime, compare the runtime measurement to the fingerprint, and in response to the measurement exceeding a threshold, executing a countermeasure operation against software ran by the separate processor.

Abstract: A system includes memory, a processor in communication with the memory. The processor is programmed to define a fingerprint that includes a baseline measurement of a physical attribute of at least a separate processor during an enrollment period of the system, wherein the enrollment period includes measuring the physical attribute of the processor prior to runtime operation, receiving a runtime measurement from a sensor regarding the physical attribute of at least the separate processor during runtime, comparing the runtime measurement of the physical attribute to the fingerprint, and outputting a multi-dimensional domain image in response to the runtime measurement.