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Publications associées (12)

Performing and Detecting Backdoor Attacks on Face Recognition Algorithms

The field of biometrics, and especially face recognition, has seen a wide-spread adoption the last few years, from access control on personal devices such as phones and laptops, to automated border controls such as in airports. The stakes are increasingly ...

EPFL2024

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Cloud computing environments increasingly provision field-programmable gate arrays (FPGAs) for their programmability and hardware-level parallelism. While FPGAs are typically used by one tenant at a time, multitenant schemes supporting spatial sharing of c ...

2024

Silent Bugs Matter: A Study of Compiler-Introduced Security Bugs

Mathias Josef Payer, Jianhao Xu

Compilers assure that any produced optimized code is semantically equivalent to the original code. However, even "correct" compilers may introduce security bugs as security properties go beyond translation correctness. Security bugs introduced by such corr ...

Berkeley2023

GLeeFuzz: FuzzingWebGL Through Error Message Guided Mutation

Mathias Josef Payer, Hui Peng

WebGL is a set of standardized JavaScript APIs for GPU accelerated graphics. Security of the WebGL interface is paramount because it exposes remote and unsandboxed access to the underlying graphics stack (including the native GL libraries and GPU drivers) ...

Berkeley2023

Trusted execution environments enable the creation of confidential and attestable enclaves that exclude the platform and service providers from the trusted base. From its initial attestable state, a stateful enclave such as a confidential database can hold ...

2022

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Public knowledge about the structure of a cryptographic system is a standard assumption in the literature and algorithms are expected to guarantee security in a setting where only the encryption key is kept secret. Nevertheless, undisclosed proprietary cry ...

2021

POSEIDON: Privacy-Preserving Federated Neural Network Learning

Jean-Pierre Hubaux, Juan Ramón Troncoso-Pastoriza, Jean-Philippe Léonard Bossuat, Apostolos Pyrgelis, David Jules Froelicher, Joao André Gomes de Sá e Sousa, Sinem Sav

In this paper, we address the problem of privacy-preserving training and evaluation of neural networks in an N-party, federated learning setting. We propose a novel system, POSEIDON, the first of its kind in the regime of privacy-preserving neural network ...

INTERNET SOC2021

This work describes a fast fully homomorphic encryption scheme over the torus (TFHE) that revisits, generalizes and improves the fully homomorphic encryption (FHE) based on GSW and its ring variants. The simplest FHE schemes consist in bootstrapped binary ...

SPRINGER2020

Viruses at solid-water interfaces: A systematic assessment of interactions driving adsorption

Tamar Kohn, Meret Aeppli, Anne Thérèse Sigstam, Antonius Magdi Hakeem Armanious, Dominik Refardt

Adsorption to solid-water interfaces is a major process governing the fate of waterborne viruses in natural and engineered systems. The relative contributions of different interaction forces to adsorption and their dependence on the physicochemical propert ...

Amer Chemical Soc2016

Strong Privacy for RFID Systems from Plaintext-Aware Encryption

Serge Vaudenay, Khaled Ouafi

he Vaudenay model for RFID privacy from Asiacrypt 2007 suffers from the impossibility to address strong privacy. It has however been shown by Ng et al. at ESORICS 2008 that the impossibility result leads to no practical threat, so that the definition from ...

Springer2012

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