Abstract Private Stream Aggregation (PSA) protocols perform secure aggregation of time-
series data without leaking information about users' inputs to the aggregator. Previous work …

The massive scale and performance demands of privacy-preserving data aggregation make
integration of security and privacy difficult. Traditional tools in private computing are not well …

Secure computing methods such as fully homomorphic encryption and hardware solutions
such as Intel Software Guard Extension (SGX) have been applied to provide security for user …

Federated learning has been identified as an efficient decentralized training paradigm for
scaling the machine learning model training on a large number of devices while …

Abstract Private Stream Aggregation (PSA) protocols allow for the secure aggregation of
time-series data, affording security and privacy to users' private data, with significantly better …

In modern times, data collected from multi-user distributed applications must be analyzed on
a massive scale to support critical business objectives. While analytics often requires the …

In the modern era of outsourced computing, guaranteeing user data security and privacy on
data in use is important for ensuring user trust and utilization. Both purely cryptographic and …