Xun Yi

Two-Factor Decryption: A Better Way to Protect Data Security and Privacy

2020 · The Computer Journal

Abstract Biometric information is unique to a human, so it would be desirable to use the biometric characteristic as the private key in a cryptographic system to protect data security and privacy. In this paper, …

Securely Outsourcing Neural Network Inference to the Cloud With Lightweight Techniques

2022 · IEEE Transactions on Dependable and Secure Computing

Neural network (NN) inference services enrich many applications, like image classification, object recognition, facial verification, and more. These NN inference services are increasingly becoming an essential offering from cloud computing providers, where end-users’ data are …

Aggregation Service for Federated Learning: An Efficient, Secure, and More Resilient Realization

2022 · IEEE Transactions on Dependable and Secure Computing

Federated learning has recently emerged as a paradigm promising the benefits of harnessing rich data from diverse sources to train high quality models, with the salient features that training datasets never leave local devices. Only …

Trustworthy Privacy-Preserving Hierarchical Ensemble and Federated Learning in Healthcare 4.0 With Blockchain

2022 · IEEE Transactions on Industrial Informatics

The advancement of internet and communication technologies has led to the era of Industry 4.0. This shift is followed by healthcare industries creating the term Healthcare 4.0. In Healthcare 4.0, the use of Internet of …

SemantiChain: A Trust Retrieval Blockchain Based on Semantic Sharding

2024 · IEEE Transactions on Information Forensics and Security

Since its inception, blockchain technology has found wide-ranging applications in various fields including agriculture, energy, and so on, owing to its immutable and decentralized nature. However, existing blockchains encounter significant challenges in scenarios that demand …

SecHeto-FL: A Secure and Resilient Federated Learning Framework for Heterogeneous IoT Networks

2026 · IEEE Transactions on Network Science and Engineering

This study introduces a semi-asynchronous web browser-based federated learning (FL) framework with secure communication, designed for heterogeneous IoT networks. The study addresses the complex challenges of FL over a large number of heterogeneous IoT devices …

Vertical Federated XGBoost with Privacy Preservation via Secure Multiparty Computation

2026 · Journal of Cybersecurity and Privacy

Gradient Boosted Decision Trees (GBDTs) are popular for their strong predictive performance. However, in domains like finance and healthcare, data are often distributed across organizations, making collaborative model training challenging due to privacy concerns. Vertical …