research
∙
04/06/2023
When approximate design for fast homomorphic computation provides differential privacy guarantees
While machine learning has become pervasive in as diversified fields as ...
share
research
∙
06/17/2022
Federated learning with incremental clustering for heterogeneous data
Federated learning enables different parties to collaboratively build a ...
research
∙
05/09/2022
Protecting Data from all Parties: Combining FHE and DP in Federated Learning
This paper tackles the problem of ensuring training data privacy in a fe...
research
∙
02/22/2021
On the robustness of randomized classifiers to adversarial examples
This paper investigates the theory of robustness against adversarial att...
research
∙
06/16/2020
SPEED: Secure, PrivatE, and Efficient Deep learning
This paper addresses the issue of collaborative deep learning with priva...
research
∙
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...
research
∙
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...
research
∙
03/10/2018
Graph-based Clustering under Differential Privacy
In this paper, we present the first differentially private clustering me...
research
∙
02/12/2018
On the Needs for Rotations in Hypercubic Quantization Hashing
The aim of this paper is to endow the well-known family of hypercubic qu...
research
∙
09/29/2016
Multi-dimensional signal approximation with sparse structured priors using split Bregman iterations
This paper addresses the structurally-constrained sparse decomposition o...
research
∙
05/29/2016
TripleSpin - a generic compact paradigm for fast machine learning computations
We present a generic compact computational framework relying on structur...
research
∙
03/21/2013
Multi-dimensional sparse structured signal approximation using split Bregman iterations
The paper focuses on the sparse approximation of signals using overcompl...
research
∙
03/04/2013
