This paper introduces Flamingo, a system for secure aggregation of data across a large set
of clients. In secure aggregation, a server sums up the private inputs of clients and obtains …

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 …

In recent years, data are typically distributed in multiple organizations while the data security
is becoming increasingly important. Federated learning (FL), which enables multiple parties …

The federated learning (FL) technique was initially developed to mitigate data privacy issues
that can arise in the traditional machine learning paradigm. While FL ensures that a user's …

Motivated by recent developments in the shuffle model of differential privacy, we propose a
new approximate shuffling functionality called Alternating Shuffle, and provide a protocol …

Privacy-preserving truth discovery, as a data aggregation algorithm that can extract reliable
results from disparate and conflicting data in a privacy-preserving manner, has received a lot …

Despite achieving good performance and wide adoption, machine learning based security
detection models (eg, malware classifiers) are subject to concept drift and evasive evolution …

Federated learning (FL) facilitates collaborative training of machine learning models among
a large number of clients while safeguarding the privacy of their local datasets. However, FL …

Our work aims to minimize interaction in secure computation due to the high cost and
challenges associated with communication rounds, particularly in scenarios with many …

In federated learning, secure aggregation (SA) protocols like Flamingo (S\&P'23) and
LERNA (ASIACRYPT'23) have achieved efficient multi-round SA in the malicious model …