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07/25/2023
Federated Heavy Hitter Recovery under Linear Sketching
Motivated by real-life deployments of multi-round federated analytics wi...
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07/20/2023
Private Federated Learning with Autotuned Compression
We propose new techniques for reducing communication in private federate...
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06/26/2023
Private Federated Learning in Gboard
This white paper describes recent advances in Gboard(Google Keyboard)'s ...
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06/15/2023
Private Federated Frequency Estimation: Adapting to the Hardness of the Instance
In federated frequency estimation (FFE), multiple clients work together ...
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05/29/2023
Federated Learning of Gboard Language Models with Differential Privacy
We train language models (LMs) with federated learning (FL) and differen...
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05/29/2023
Unleashing the Power of Randomization in Auditing Differentially Private ML
We present a rigorous methodology for auditing differentially private ma...
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04/04/2023
Privacy Amplification via Compression: Achieving the Optimal Privacy-Accuracy-Communication Trade-off in Distributed Mean Estimation
Privacy and communication constraints are two major bottlenecks in feder...
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03/31/2023
Differentially Private Stream Processing at Scale
We design, to the best of our knowledge, the first differentially privat...
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02/06/2023
One-shot Empirical Privacy Estimation for Federated Learning
Privacy auditing techniques for differentially private (DP) algorithms a...
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07/09/2022
The Poisson binomial mechanism for secure and private federated learning
We introduce the Poisson Binomial mechanism (PBM), a discrete differenti...
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06/07/2022
Histogram Estimation under User-level Privacy with Heterogeneous Data
We study the problem of histogram estimation under user-level differenti...
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03/07/2022
The Fundamental Price of Secure Aggregation in Differentially Private Federated Learning
We consider the problem of training a d dimensional model with distribut...
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01/13/2022
Privacy-Utility Trades in Crowdsourced Signal Map Obfuscation
Cellular providers and data aggregating companies crowdsource celluar si...
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11/03/2021
Towards Sparse Federated Analytics: Location Heatmaps under Distributed Differential Privacy with Secure Aggregation
We design a scalable algorithm to privately generate location heatmaps o...
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10/29/2021
Optimal Compression of Locally Differentially Private Mechanisms
Compressing the output of ϵ-locally differentially private (LDP) randomi...
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10/11/2021
The Skellam Mechanism for Differentially Private Federated Learning
We introduce the multi-dimensional Skellam mechanism, a discrete differe...
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10/07/2021
Pointwise Bounds for Distribution Estimation under Communication Constraints
We consider the problem of estimating a d-dimensional discrete distribut...
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08/29/2021
Lower Bounds for the Minimum Mean-Square Error via Neural Network-based Estimation
The minimum mean-square error (MMSE) achievable by optimal estimation of...
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08/23/2021
Back to the Drawing Board: A Critical Evaluation of Poisoning Attacks on Federated Learning
While recent works have indicated that federated learning (FL) is vulner...
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06/16/2021
Breaking The Dimension Dependence in Sparse Distribution Estimation under Communication Constraints
We consider the problem of estimating a d-dimensional s-sparse discrete ...
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05/11/2021
On the Renyi Differential Privacy of the Shuffle Model
The central question studied in this paper is Renyi Differential Privacy...
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02/12/2021
The Distributed Discrete Gaussian Mechanism for Federated Learning with Secure Aggregation
We consider training models on private data that is distributed across u...
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08/17/2020
Shuffled Model of Federated Learning: Privacy, Communication and Accuracy Trade-offs
We consider a distributed empirical risk minimization (ERM) optimization...
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08/14/2020
Dimension Independence in Unconstrained Private ERM via Adaptive Preconditioning
In this paper we revisit the problem of private empirical risk minimziat...
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07/22/2020
Breaking the Communication-Privacy-Accuracy Trilemma
Two major challenges in distributed learning and estimation are 1) prese...
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07/13/2020
Privacy Amplification via Random Check-Ins
Differentially Private Stochastic Gradient Descent (DP-SGD) forms a fund...
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01/27/2020
DP-CGAN: Differentially Private Synthetic Data and Label Generation
Generative Adversarial Networks (GANs) are one of the well-known models ...
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12/10/2019
Advances and Open Problems in Federated Learning
Federated learning (FL) is a machine learning setting where many clients...
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11/18/2019
Can You Really Backdoor Federated Learning?
The decentralized nature of federated learning makes detecting and defen...
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11/15/2019
Generative Models for Effective ML on Private, Decentralized Datasets
To improve real-world applications of machine learning, experienced mode...
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11/08/2019
Theoretical Guarantees for Model Auditing with Finite Adversaries
Privacy concerns have led to the development of privacy-preserving appro...
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10/31/2019
Context-Aware Local Differential Privacy
Local differential privacy (LDP) is a strong notion of privacy for indiv...
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09/27/2019
Learning Generative Adversarial RePresentations (GAP) under Fairness and Censoring Constraints
We present Generative Adversarial rePresentations (GAP) as a data-driven...
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06/05/2019
A Tunable Loss Function for Classification
Recently, a parametrized class of loss functions called α-loss, α∈ [1,∞]...
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02/22/2019
Federated Heavy Hitters Discovery with Differential Privacy
The discovery of heavy hitters (most frequent items) in user-generated d...
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02/12/2019
A Tunable Loss Function for Binary Classification
We present α-loss, α∈ [1,∞], a tunable loss function for binary classifi...
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09/21/2018
Understanding Compressive Adversarial Privacy
Designing a data sharing mechanism without sacrificing too much privacy ...
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08/04/2018
On the Optimality of the Kautz-Singleton Construction in Probabilistic Group Testing
We consider the probabilistic group testing problem where d random defec...
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07/13/2018
Generative Adversarial Privacy
We present a data-driven framework called generative adversarial privacy...
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04/23/2018
Siamese Generative Adversarial Privatizer for Biometric Data
State-of-the-art machine learning algorithms can be fooled by carefully ...
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01/18/2018
On the Contractivity of Privacy Mechanisms
We present a novel way to compare the statistical cost of privacy mechan...
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10/26/2017
Context-Aware Generative Adversarial Privacy
Preserving the utility of published datasets while simultaneously provid...
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02/24/2016
