As an emerging training model with neural networks, federated learning has received
widespread attention due to its ability to update parameters without collecting users' raw …

Important decisions about people are increasingly made by algorithms: Votes are counted;
voter rolls are purged; financial aid decisions are made; taxpayers are chosen for audits; air …

With the proliferation of machine learning, the cloud server has been employed to collect
massive data and train machine learning models. Several privacy-preserving machine …

We present a zero-knowledge argument for NP with low communication complexity, low
concrete cost for both the prover and the verifier, and no trusted setup, based on standard …

In privacy-preserving cross-device federated learning, users train a global model on their
local data and submit encrypted local models, while an untrusted central server aggregates …

Mobile crowdsensing enables convenient sensory data collection from a large number of
mobile devices and has found various applications. In the real practice, however, the …

Fully Homomorphic Encryption (FHE) is seeing increasing real-world deployment to protect
data in use by allowing computation over encrypted data. However, the same malleability …

In a secure data sharing system, the keyword search over encrypted files is a basic need of
a user with appropriate privileges. Although the traditional searchable encryption technique …

Federated learning (FL) protects training data in clients by collaboratively training local
machine learning models of clients for a global model, instead of directly feeding the training …

The narrowband Internet of Things (NB-IoT) system has become an integral part of the future
fifth generation (5G) network. Although the NB-IoT system has gradually been improved in …