6 papers
Mitigating Noise Detriment in Differentially Private Federated Learning with Model Pre-training
Huitong Jin, Yipeng Zhou, Quan Z. Sheng +2
Differentially Private Federated Learning (DPFL) strengthens privacy protection by perturbing model gradients with noise, though at the cost of reduced accuracy. Although prior emp…
ExClique: An Express Consensus Algorithm for High-Speed Transaction Process in Blockchains
Chonghe Zhao, Yipeng Zhou, Shengli Zhang +3
Proof of Authority (PoA) plays a pivotal role in blockchains for reaching consensus. Clique, which selects consensus nodes to generate blocks with a pre-determined order, is the mo…
A Survey on Privacy-Preserving Caching at Network Edge: Classification, Solutions, and Challenges
Xianzhi Zhang, Yipeng Zhou, Di Wu +4
Caching content at the edge network is a popular and effective technique widely deployed to alleviate the burden of network backhaul, shorten service delay and improve service qual…
PPVF: An Efficient Privacy-Preserving Online Video Fetching Framework with Correlated Differential Privacy
Xianzhi Zhang, Yipeng Zhou, Di Wu +3
Online video streaming has evolved into an integral component of the contemporary Internet landscape. Yet, the disclosure of user requests presents formidable privacy challenges. A…
The Power of Bias: Optimizing Client Selection in Federated Learning with Heterogeneous Differential Privacy
Jiating Ma, Yipeng Zhou, Qi Li +3
To preserve the data privacy, the federated learning (FL) paradigm emerges in which clients only expose model gradients rather than original data for conducting model training. To…
DEthna: Accurate Ethereum Network Topology Discovery with Marked Transactions
Chonghe Zhao, Yipeng Zhou, Shengli Zhang +3
In Ethereum, the ledger exchanges messages along an underlying Peer-to-Peer (P2P) network to reach consistency. Understanding the underlying network topology of Ethereum is crucial…