Robert Boschgmbh

Stefan Gehrer

Jorge Guajardo Merchan

Christian Zimmermann

Sven Trieflinger

Shalabh Jain

Betül Durak

Filipe J Cabrita Condessa

Timo Lothspeich

Wan-Yi Lin

João D Semedo

S Alireza Golestaneh

Saraswathy Ramanathapuram Vancheeswaran

Fatma Betül Durak

Christian Matthias Didong

Pradeep Pappachan

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.

System and method for secure and performant ECU pairing

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.

Computerized systems and methods for secure execution and offloading of computations between trusted and untrusted devices

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.

Method and system for detection and protection of personal data in autonomous driving

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.

Methods and systems for managing personal data associated with image processing

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.

System and method for intrusion detection on a physical level using an internal analog to digital converter

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.

System and method for processing a data subject rights request using biometric data matching

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.

Learning semantic segmentation models in the absence of a portion of class labels

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.

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Pittsburgh, PA, United States of America

Stefan Gehrer