Xun Yi

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Professor

RMIT University

Australia

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About

Professor Xun Yi is a faculty member at RMIT University in Australia. His research focuses on areas including adversarial attacks in computer vision, session-based recommendation systems, IoT security, smart agriculture, and decentralized identity management. He has contributed to numerous recent publications exploring innovative solutions in these fields.

Recent Grants

Grant: Close

Privacy-preserving online user matching

Open Date: 2018-01-01

Close Date: 2020-01-01

Grant: Close

Privacy-Preserving Cloud Data Mining-as-a-Service

Open Date: 2017-01-01

Close Date: 2020-01-01

Grant: Close

Enhancing Security and Privacy in IoT

Open Date: 2017-01-01

Close Date: 2019-01-01

Grant: Close

Security and Privacy of Individual Data Used to Extract Public Information

Open Date: 2016-01-01

Close Date: 2018-01-01

Grant: Close

Privacy Preserving Data Sharing in Electronic Health Environment

Open Date: 2015-01-01

Close Date: 2017-01-01

Xun Yi

Research Interests

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Contact this professor

About

Recent Grants

Articles (26)

Transferable adversarial attacks on human pose estimation: A regularization and pruning framework

Unified Empirical Evaluation and Comparison of Session-based Recommendation Algorithms

Remote sensing revolutionizing agriculture: Toward a new frontier

Secure Reputation-Based Authentication With Malicious Detection in VANETs

Toward LLM-Driven Adaptive Policy Orchestration for Host-Based Intrusion Detection Systems in IoT Environments

Blockchain‐Empowered H‐CPS Architecture for Smart Agriculture

DidTrust: Privacy-Preserving Trust Management for Decentralized Identity

EPSMR: An efficient privacy-preserving semantic-aware multi-keyword ranked search scheme in cloud

QARMA-FL: Quality-Aware Robust Model Aggregation for Mobile Crowdsourcing

Secure Internet of Things in Cloud Computing via Puncturable Attribute-Based Encryption With User Revocation

A Robust and Secure Data Access Scheme for Satellite-Assisted Internet of Things With Content Adaptive Addressing

Auditable and Verifiable Federated Learning Based on Blockchain-Enabled Decentralization

FedPA: Generator-Based Heterogeneous Federated Prototype Adversarial Learning

GoCrowd: Obliviously Aggregating Crowd Wisdom With Quality Awareness in Crowdsourcing

Lightweight Verifiable Privacy-Preserving Data Aggregation for Smart Grids

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

Password-authenticated proofs of retrievability for multiple devices checking cloud data

Blockchain-Based AI-Enabled Industry 4.0 CPS Protection Against Advanced Persistent Threat

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

Blockchain-Based Federated Learning With Secure Aggregation in Trusted Execution Environment for Internet-of-Things

Toward Trustworthy and Privacy-Preserving Federated Deep Learning Service Framework for Industrial Internet of Things

Authenticated Data Sharing With Privacy Protection and Batch Verification for Healthcare IoT

Securely Outsourcing Neural Network Inference to the Cloud With Lightweight Techniques

A Security-Enhanced Certificateless Conditional Privacy-Preserving Authentication Scheme for Vehicular Ad Hoc Networks

Blockchain-Based Federated Learning With SMPC Model Verification Against Poisoning Attack for Healthcare Systems

Towards Sustainable Trust: A Practical SGX Aided Anonymous Reputation System

Collaborators (10)

Xingliang Yuan

Xiuzhen (Jenny) Zhang

Mohammed Atiquzzaman

Xiaoning Liu

Ziaur Rahman

Md. Jalil Piran

Xuechao Yang

Mohammad Saidur Rahman

Ibrahim Khalil

Kwok-Yan Lam

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