Robert Boschgmbh

Global Id Sa

Betül Durak

Jorge Guajardo Merchan

Christian Zimmermann

Sven Trieflinger

Stefan Gehrer

Serge Vaudenay

Lambert Sonna Momo

Fatih Balli

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

A method for recognizing and/or identifying a user () with a chip (C) in an electronic identity object storing a digital identity (), the method comprising steps of: —establishing a wireless or electrical connection between the electronic identity object (C) and a verification terminal (T); —verifying, in the electronic identity object, if the verification terminal is authorized to communicate with the electronic identity object (C), and in response of a positive verification sharing a secret (K): using the shared secret (K) for establishing an encrypted symmetric data link () between the electronic identity object and the verification terminal (T); transmitting, through the encrypted data link (), said digital identity () stored in the electronic identity object to the verification terminal (T); and verifying in the verification terminal (T) the authenticity of said digital identity ().

Method, electronic identity object, and terminal for recognizing and/or identifying a user

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 distributed computer network utilizing cryptography that includes one or more processors, wherein the one or more processors are programmed to receive a secret input state and one or more tuples, mask the secret input state with the one or more tuple and utilize a reveal to compute a masked input, compute six multiplications on the masked input, compute multiplication of two secret values to output an intermediate output, mask a third secret value from the intermediate output and reveal the third secret value to compute an interaction, compute a multiparty-computation multiplication with the interaction, and output a final secret value in response to computing the multiparty-computation multiplication.

System and method for improving the efficiency of advanced encryption standard in multi-party computation with precomputed data

An apparatus includes a processor programmed to define an input matrix and kernel matrix based upon the encrypted data, identify an algebraic structure of an encryption method applied to the encrypted data, determine a primitive root of unity in the algebraic structure in response to an input matrix size and a kernel matrix size, transform the input matrix and kernel matrix utilizing the primitive root of unity into a transformed input matrix and a transformed kernel matrix, compute an element-wise multiplication of the transformed input matrix and transformed kernel matrix, apply a reverse discrete Fourier transformation, and output a convolution of the input matrix and the kernel matrix based upon the encrypted data.

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Pittsburgh, PA, United States of America

Betül Durak