
-
The Distributed Discrete Gaussian Mechanism for Federated Learning with Secure Aggregation
We consider training models on private data that is distributed across u...
read it
-
Shuffled Model of Federated Learning: Privacy, Communication and Accuracy Trade-offs
We consider a distributed empirical risk minimization (ERM) optimization...
read it
-
Dimension Independence in Unconstrained Private ERM via Adaptive Preconditioning
In this paper we revisit the problem of private empirical risk minimziat...
read it
-
Breaking the Communication-Privacy-Accuracy Trilemma
Two major challenges in distributed learning and estimation are 1) prese...
read it
-
Privacy Amplification via Random Check-Ins
Differentially Private Stochastic Gradient Descent (DP-SGD) forms a fund...
read it
-
DP-CGAN: Differentially Private Synthetic Data and Label Generation
Generative Adversarial Networks (GANs) are one of the well-known models ...
read it
-
Advances and Open Problems in Federated Learning
Federated learning (FL) is a machine learning setting where many clients...
read it
-
Can You Really Backdoor Federated Learning?
The decentralized nature of federated learning makes detecting and defen...
read it
-
Generative Models for Effective ML on Private, Decentralized Datasets
To improve real-world applications of machine learning, experienced mode...
read it
-
Theoretical Guarantees for Model Auditing with Finite Adversaries
Privacy concerns have led to the development of privacy-preserving appro...
read it
-
Context-Aware Local Differential Privacy
Local differential privacy (LDP) is a strong notion of privacy for indiv...
read it
-
Learning Generative Adversarial RePresentations (GAP) under Fairness and Censoring Constraints
We present Generative Adversarial rePresentations (GAP) as a data-driven...
read it
-
A Tunable Loss Function for Classification
Recently, a parametrized class of loss functions called α-loss, α∈ [1,∞]...
read it
-
Federated Heavy Hitters Discovery with Differential Privacy
The discovery of heavy hitters (most frequent items) in user-generated d...
read it
-
A Tunable Loss Function for Binary Classification
We present α-loss, α∈ [1,∞], a tunable loss function for binary classifi...
read it
-
Understanding Compressive Adversarial Privacy
Designing a data sharing mechanism without sacrificing too much privacy ...
read it
-
On the Optimality of the Kautz-Singleton Construction in Probabilistic Group Testing
We consider the probabilistic group testing problem where d random defec...
read it
-
Generative Adversarial Privacy
We present a data-driven framework called generative adversarial privacy...
read it
-
Siamese Generative Adversarial Privatizer for Biometric Data
State-of-the-art machine learning algorithms can be fooled by carefully ...
read it
-
On the Contractivity of Privacy Mechanisms
We present a novel way to compare the statistical cost of privacy mechan...
read it
-
Context-Aware Generative Adversarial Privacy
Preserving the utility of published datasets while simultaneously provid...
read it
-
Discrete Distribution Estimation under Local Privacy
The collection and analysis of user data drives improvements in the app ...
read it