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Found 13 papers, 2 papers with code
Federated Dataset Dictionary Learning for Multi-Source Domain Adaptation
The proposed framework, FedDaDiL, tackles the resulting challenge through dictionary learning of empirical distributions.
When approximate design for fast homomorphic computation provides differential privacy guarantees
While machine learning has become pervasive in as diversified fields as industry, healthcare, social networks, privacy concerns regarding the training data have gained a critical importance.
Protecting Data from all Parties: Combining FHE and DP in Federated Learning
This paper tackles the problem of ensuring training data privacy in a federated learning context.
On the robustness of randomized classifiers to adversarial examples
This paper investigates the theory of robustness against adversarial attacks.
SPEED: Secure, PrivatE, and Efficient Deep learning
Based on collaborative learning, differential privacy and homomorphic encryption, the proposed approach advances state-of-the-art of private deep learning against a wider range of threats, in particular the honest-but-curious server assumption.
A unified view on differential privacy and robustness to adversarial examples
This short note highlights some links between two lines of research within the emerging topic of trustworthy machine learning: differential privacy and robustness to adversarial examples.
Theoretical evidence for adversarial robustness through randomization
This paper investigates the theory of robustness against adversarial attacks.
Graph-based Clustering under Differential Privacy
In this paper, we present the first differentially private clustering method for arbitrary-shaped node clusters in a graph.
On the Needs for Rotations in Hypercubic Quantization Hashing
The aim of this paper is to endow the well-known family of hypercubic quantization hashing methods with theoretical guarantees.
Streaming Binary Sketching based on Subspace Tracking and Diagonal Uniformization
We demonstrate the quality of our binary sketches through experiments on real data for the nearest neighbors search task in the online setting.
Graph sketching-based Space-efficient Data Clustering
In this paper, we address the problem of recovering arbitrary-shaped data clusters from datasets while facing \emph{high space constraints}, as this is for instance the case in many real-world applications when analysis algorithms are directly deployed on resources-limited mobile devices collecting the data.
Multi-dimensional signal approximation with sparse structured priors using split Bregman iterations
This paper addresses the structurally-constrained sparse decomposition of multi-dimensional signals onto overcomplete families of vectors, called dictionaries.
Multi-dimensional sparse structured signal approximation using split Bregman iterations
An extensive empirical evaluation shows how the proposed approach compares to the state of the art depending on the signal features.
