activity
20212024
most citedWhen Crowdsensing Meets Federated Learning: Privacy-Preserving Mobile Crowdsensing System

14 citations · 29 across the 7 of their papers we have counts for

collaborators

7 papers

cs.CR2024

MORSE: An Efficient Homomorphic Secret Sharing Scheme Enabling Non-Linear Operation

Weiquan Deng, Bowen Zhao, Yang Xiao +3

Homomorphic secret sharing (HSS) enables two servers to locally perform functions on encrypted data directly and obtain the results in the form of shares. A Paillier-based HSS solu…

cs.CR2023

SOCI^+: An Enhanced Toolkit for Secure OutsourcedComputation on Integers

Bowen Zhao, Weiquan Deng, Xiaoguo Li +3

Secure outsourced computation is critical for cloud computing to safeguard data confidentiality and ensure data usability. Recently, secure outsourced computation schemes following…

cs.NE2023

CEC: Crowdsourcing-based Evolutionary Computation for Distributed Optimization

Feng-Feng Wei, Wei-Neng Chen, Xiao-Qi Guo +3

Crowdsourcing is an emerging computing paradigm that takes advantage of the intelligence of a crowd to solve complex problems effectively. Besides collecting and processing data, i…

cs.NE2023★ 1 cited

When Evolutionary Computation Meets Privacy

Bowen Zhao, Wei-Neng Chen, Xiaoguo Li +3

Recently, evolutionary computation (EC) has been promoted by machine learning, distributed computing, and big data technologies, resulting in new research directions of EC like dis…

cs.CR2023★ 8 cited

A Survey of Secure Computation Using Trusted Execution Environments

Xiaoguo Li, Bowen Zhao, Guomin Yang +3

As an essential technology underpinning trusted computing, the trusted execution environment (TEE) allows one to launch computation tasks on both on- and off-premises data while as…

cs.NE2022★ 6 cited

Evolution as a Service: A Privacy-Preserving Genetic Algorithm for Combinatorial Optimization

Bowen Zhao, Wei-Neng Chen, Feng-Feng Wei +3

Evolutionary algorithms (EAs), such as the genetic algorithm (GA), offer an elegant way to handle combinatorial optimization problems (COPs). However, limited by expertise and reso…

cs.CR2021★ 14 cited

When Crowdsensing Meets Federated Learning: Privacy-Preserving Mobile Crowdsensing System

Bowen Zhao, Ximeng Liu, Wei-neng Chen

Mobile crowdsensing (MCS) is an emerging sensing data collection pattern with scalability, low deployment cost, and distributed characteristics. Traditional MCS systems suffer from…