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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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05/18/2023
Amplification by Shuffling without Shuffling
Motivated by recent developments in the shuffle model of differential pr...
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01/15/2023
UN Handbook on Privacy-Preserving Computation Techniques
This paper describes privacy-preserving approaches for the statistical a...
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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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09/15/2021
MPC-Friendly Commitments for Publicly Verifiable Covert Security
We address the problem of efficiently verifying a commitment in a two-pa...
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04/12/2020
PrivEdge: From Local to Distributed Private Training and Prediction
Machine Learning as a Service (MLaaS) operators provide model training a...
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02/03/2020
Private Summation in the Multi-Message Shuffle Model
The shuffle model of differential privacy (Erlingsson et al. SODA 2019; ...
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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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11/06/2019
Data Generation for Neural Programming by Example
Programming by example is the problem of synthesizing a program from a s...
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10/09/2019
Private Protocols for U-Statistics in the Local Model and Beyond
In this paper, we study the problem of computing U-statistics of degree ...
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09/24/2019
Improved Summation from Shuffling
A protocol by Ishai et al. (FOCS 2006) showing how to implement distribu...
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07/08/2019
QUOTIENT: Two-Party Secure Neural Network Training and Prediction
Recently, there has been a wealth of effort devoted to the design of sec...
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06/20/2019
Differentially Private Summation with Multi-Message Shuffling
In recent work, Cheu et al. (Eurocrypt 2019) proposed a protocol for n-p...
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03/07/2019
The Privacy Blanket of the Shuffle Model
This work studies differential privacy in the context of the recently pr...
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06/09/2018
TAPAS: Tricks to Accelerate (encrypted) Prediction As a Service
Machine learning methods are widely used for a variety of prediction pro...
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06/08/2018
Blind Justice: Fairness with Encrypted Sensitive Attributes
Recent work has explored how to train machine learning models which do n...
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05/31/2018
How to Simulate It in Isabelle: Towards Formal Proof for Secure Multi-Party Computation
In cryptography, secure Multi-Party Computation (MPC) protocols allow pa...
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02/15/2018