Nowadays, with the development of artificial intelligence (AI), privacy issues attract wide
attention from society and individuals. It is desirable to make the data available but invisible …

Federated learning, involving a central server and multiple clients, aims to keep data local
but raises privacy concerns like data exposure and participation privacy. Secure …

This paper introduces LERNA, a new framework for single-server secure aggregation. Our
protocols are tailored to the setting where multiple consecutive aggregation phases are …

Public randomness is a fundamental component in many cryptographic protocols and
distributed systems and often plays a crucial role in ensuring their security, fairness, and …

Federated Unlearning (FU) is gaining prominence for its capability to eliminate influences of
specific users' data from trained global Federated Learning (FL) models. A straightforward …

Federated learning is a computing paradigm that enhances privacy by enabling multiple
parties to collaboratively train a machine learning model without revealing personal data …

This paper proposes POPSTAR, a new lightweight protocol for the private computation of
heavy hitters, also known as a private threshold reporting system. In such a protocol, the …

Federated learning is known for its capability to safeguard participants' data privacy.
However, recently emerged model inversion attacks (MIAs) have shown that a malicious …

Homomorphic Encryption (HE) facilitates the preservation of privacy in federated learning
(FL) aggregation. However, HE imposes significant computational and communication …

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 …