Search Results for author:
Found 18 papers, 4 papers with code
Rényi Pufferfish Privacy: General Additive Noise Mechanisms and Privacy Amplification by Iteration
Pufferfish privacy is a flexible generalization of differential privacy that allows to model arbitrary secrets and adversary's prior knowledge about the data.
Improved Stability and Generalization Guarantees of the Decentralized SGD Algorithm
On the contrary, we show, for convex, strongly convex and non-convex functions, that D-SGD can always recover generalization bounds analogous to those of classical SGD, suggesting that the choice of graph does not matter.
Differential Privacy has Bounded Impact on Fairness in Classification
We theoretically study the impact of differential privacy on fairness in classification.
FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare Settings
In this work, we propose a novel cross-silo dataset suite focused on healthcare, FLamby (Federated Learning AMple Benchmark of Your cross-silo strategies), to bridge the gap between theory and practice of cross-silo FL.
Collaborative Algorithms for Online Personalized Mean Estimation
We study the case where some of the distributions have the same mean, and the agents are allowed to actively query information from other agents.
High-Dimensional Private Empirical Risk Minimization by Greedy Coordinate Descent
In this paper, we study differentially private empirical risk minimization (DP-ERM).
Refined Convergence and Topology Learning for Decentralized SGD with Heterogeneous Data
One of the key challenges in decentralized and federated learning is to design algorithms that efficiently deal with highly heterogeneous data distributions across agents.
Differentially Private Speaker Anonymization
We remove speaker information from these attributes by introducing differentially private feature extractors based on an autoencoder and an automatic speech recognizer, respectively, trained using noise layers.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Retrieving Speaker Information from Personalized Acoustic Models for Speech Recognition
The widespread of powerful personal devices capable of collecting voice of their users has opened the opportunity to build speaker adapted speech recognition system (ASR) or to participate to collaborative learning of ASR.
Privacy attacks for automatic speech recognition acoustic models in a federated learning framework
This paper investigates methods to effectively retrieve speaker information from the personalized speaker adapted neural network acoustic models (AMs) in automatic speech recognition (ASR).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Differentially Private Coordinate Descent for Composite Empirical Risk Minimization
In this paper, we propose Differentially Private proximal Coordinate Descent (DP-CD), a new method to solve composite DP-ERM problems.
Design Choices for X-vector Based Speaker Anonymization
The recently proposed x-vector based anonymization scheme converts any input voice into that of a random pseudo-speaker.
Privacy-Preserving Adversarial Representation Learning in ASR: Reality or Illusion?
In this paper, we focus on the protection of speaker identity and study the extent to which users can be recognized based on the encoded representation of their speech as obtained by a deep encoder-decoder architecture trained for ASR.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Evaluating Voice Conversion-based Privacy Protection against Informed Attackers
In this paper, we investigate anonymization methods based on voice conversion.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Fully Decentralized Joint Learning of Personalized Models and Collaboration Graphs
We consider the fully decentralized machine learning scenario where many users with personal datasets collaborate to learn models through local peer-to-peer exchanges, without a central coordinator.
Personalized and Private Peer-to-Peer Machine Learning
The rise of connected personal devices together with privacy concerns call for machine learning algorithms capable of leveraging the data of a large number of agents to learn personalized models under strong privacy requirements.
Decentralized Collaborative Learning of Personalized Models over Networks
We consider a set of learning agents in a collaborative peer-to-peer network, where each agent learns a personalized model according to its own learning objective.
Fiedler Random Fields: A Large-Scale Spectral Approach to Statistical Network Modeling
Statistical models for networks have been typically committed to strong prior assumptions concerning the form of the modeled distributions.
