Differential privacy is often studied in one of two models. In the central model, a single
analyzer has the responsibility of performing a privacy-preserving computation on data. But …

Most works in learning with differential privacy (DP) have focused on the setting where each
user has a single sample. In this work, we consider the setting where each user holds $ m …

We consider the computation of sparse,(ε, ϑ)-differentially private~(DP) histograms in the
two-server model of secure multi-party computation~(MPC), which has recently gained …

The shuffle model of differential privacy has attracted attention in the literature due to it being
a middle ground between the well-studied central and local models. In this work, we study …

Differential privacy (DP) is a formal notion for quantifying the privacy loss of algorithms.
Algorithms in the central model of DP achieve high accuracy but make the strongest trust …

The shuffle model has recently emerged as a popular setting for differential privacy, where
clients can communicate with a central server using anonymous channels or an …

Federated learning (FL) is a type of collaborative machine learning where participating
peers/clients process their data locally, sharing only updates to the collaborative model. This …

The shuffle model of differential privacy provides promising privacy-utility balances in
decentralized, privacy-preserving data analysis. However, the current analyses of privacy …

Secure aggregation based on masked encryption is a crucial technique for data collection in
the Internet of Things (IoT) as it employs a lightweight style to enable global data …

Local differential privacy (LDP) is a variant of differential privacy (DP) that avoids the
necessity of a trusted central curator, at the expense of a worse trade-off between privacy …