18 citations · 19 across the 7 of their papers we have counts for
8 papers
QSplitFL: Capability Aware Deep Q-Learning for Optimal Split Point Selection in Split Federated Learning
Nazmus Shakib Shadin, Xinyue Zhang, Jingyi Wang +1
Federated Learning (FL) combined with Split Learning (SL) is a privacy preserving paradigm that enables training deep neural networks (DNNs) on resource constrained devices while r…
Differential Privacy Preserving Distributed Quantum Computing
Hui Zhong, Keyi Ju, Jiachen Shen +5
Existing quantum computers can only operate with hundreds of qubits in the Noisy Intermediate-Scale Quantum (NISQ) state, while quantum distributed computing (QDC) is regarded as a…
Bridging Quantum Computing and Differential Privacy: Insights into Quantum Computing Privacy
Yusheng Zhao, Hui Zhong, Xinyue Zhang +3
While quantum computing has strong potential in data-driven fields, the privacy issue of sensitive or valuable information involved in the quantum algorithm should be considered. D…
Tuning Quantum Computing Privacy through Quantum Error Correction
Hui Zhong, Keyi Ju, Manojna Sistla +4
Quantum computing is a promising paradigm for efficiently solving large and high-complexity problems. To protect quantum computing privacy, pioneering research efforts proposed to…
Harnessing Inherent Noises for Privacy Preservation in Quantum Machine Learning
Keyi Ju, Xiaoqi Qin, Hui Zhong +3
Quantum computing revolutionizes the way of solving complex problems and handling vast datasets, which shows great potential to accelerate the machine learning process. However, da…
Differential Privacy Preserving Quantum Computing via Projection Operator Measurements
Yuqing Li, Yusheng Zhao, Xinyue Zhang +3
Quantum computing has been widely applied in various fields, such as quantum physics simulations, quantum machine learning, and big data analysis. However, in the domains of data-d…
Evaluation of Inference Attack Models for Deep Learning on Medical Data
Maoqiang Wu, Xinyue Zhang, Jiahao Ding +4
Deep learning has attracted broad interest in healthcare and medical communities. However, there has been little research into the privacy issues created by deep networks trained f…
Differentially Private and Fair Classification via Calibrated Functional Mechanism
Jiahao Ding, Xinyue Zhang, Xiaohuan Li +3
Machine learning is increasingly becoming a powerful tool to make decisions in a wide variety of applications, such as medical diagnosis and autonomous driving. Privacy concerns re…