Description
This talk is about inaccurate assumptions, unrealistic trust models, and flawed methodologies affecting current collaborative machine learning techniques. In the presentation, we cover different security issues concerning both emerging approaches and well-established solutions in privacy-preserving collaborative machine learning. We start by discussing the inherent insecurity of Split Learning and peer-to-peer collaborative learning. Then, we talk about the soundness of current Secure Aggregation protocols in Federated Learning, showing that those do not provide any additional level of privacy to users. Ultimately, the objective of this talk is to highlight the general errors and flawed approaches we all should avoid in devising and implementing "privacy-preserving collaborative machine learning".
Infos pratiques
Prochains exposés
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CHERIoT RTOS: An OS for Fine-Grained Memory-Safe Compartments on Low-Cost Embedded Devices
Orateur : Hugo Lefeuvre - The University of British Columbia
Embedded systems do not benefit from strong memory protection, because they are designed to minimize cost. At the same time, there is increasing pressure to connect embedded devices to the internet, where their vulnerable nature makes them routinely subject to compromise. This fundamental tension leads to the current status-quo where exploitable devices put individuals and critical infrastructure[…]-
SoSysec
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Compartmentalization
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Operating system and virtualization
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Hardware/software co-design
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Hardware architecture
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