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09/11/2023
Practical Homomorphic Aggregation for Byzantine ML
Due to the large-scale availability of data, machine learning (ML) algor...
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08/04/2023
On the Inherent Anonymity of Gossiping
Detecting the source of a gossip is a critical issue, related to identif...
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02/09/2023
Distributed Learning with Curious and Adversarial Machines
The ubiquity of distributed machine learning (ML) in sensitive public do...
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09/30/2022
SoK: On the Impossible Security of Very Large Foundation Models
Large machine learning models, or so-called foundation models, aim to se...
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09/22/2022
Making Byzantine Decentralized Learning Efficient
Decentralized-SGD (D-SGD) distributes heavy learning tasks across multip...
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06/03/2022
Towards Evading the Limits of Randomized Smoothing: A Theoretical Analysis
Randomized smoothing is the dominant standard for provable defenses agai...
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05/24/2022
Byzantine Machine Learning Made Easy by Resilient Averaging of Momentums
Byzantine resilience emerged as a prominent topic within the distributed...
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05/20/2022
Towards Consistency in Adversarial Classification
In this paper, we study the problem of consistency in the context of adv...
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10/08/2021
Combining Differential Privacy and Byzantine Resilience in Distributed SGD
Privacy and Byzantine resilience (BR) are two crucial requirements of mo...
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02/22/2021
On the robustness of randomized classifiers to adversarial examples
This paper investigates the theory of robustness against adversarial att...
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02/16/2021
Differential Privacy and Byzantine Resilience in SGD: Do They Add Up?
This paper addresses the problem of combining Byzantine resilience with ...
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02/13/2021
Mixed Nash Equilibria in the Adversarial Examples Game
This paper tackles the problem of adversarial examples from a game theor...
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12/04/2020
Advocating for Multiple Defense Strategies against Adversarial Examples
It has been empirically observed that defense mechanisms designed to pro...
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06/16/2020
SPEED: Secure, PrivatE, and Efficient Deep learning
This paper addresses the issue of collaborative deep learning with priva...
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02/26/2020
Randomization matters. How to defend against strong adversarial attacks
Is there a classifier that ensures optimal robustness against all advers...
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06/19/2019
A unified view on differential privacy and robustness to adversarial examples
This short note highlights some links between two lines of research with...
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03/25/2019
Robust Neural Networks using Randomized Adversarial Training
Since the discovery of adversarial examples in machine learning, researc...
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02/04/2019
Theoretical evidence for adversarial robustness through randomization: the case of the Exponential family
This paper investigates the theory of robustness against adversarial att...
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03/10/2018
Graph-based Clustering under Differential Privacy
In this paper, we present the first differentially private clustering me...
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01/19/2018
