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- Centre for Quantum Computation and Communication TechnologyAU39 papers
- The University of QueenslandAU25 papers
- Australian Research CouncilAU23 papers
- Australian National UniversityAU12 papers
- University of Technology SydneyAU9 papers
- UNSW SydneyAU9 papers
- Centre National de la Recherche ScientifiqueFR8 papers
- Commonwealth Scientific and Industrial Research OrganisationAU8 papers
- Monash UniversityAU7 papers
- Macquarie UniversityAU6 papers
- National University of SingaporeSG6 papers
- University of OxfordGB6 papers
6 papers · 1 filter
QAE-BAC: Achieving Quantifiable Anonymity and Efficiency in Blockchain-Based Access Control with Attribute
Jie Zhang, Xiaohong Li, Mengke Zhang +4
Blockchain-based Attribute-Based Access Control (BC-ABAC) offers a decentralized paradigm for secure data governance but faces two inherent challenges: the transparency of blockcha…
Character-Level Perturbations Disrupt LLM Watermarks
Zhaoxi Zhang, Xiaomei Zhang, Yanjun Zhang +5
Large Language Model (LLM) watermarking embeds detectable signals into generated text for copyright protection, misuse prevention, and content detection. While prior studies evalua…
VeriFuzzy: A Dynamic Verifiable Fuzzy Search Service for Encrypted Cloud Data
Jie Zhang, Xiaohong Li, Man Zheng +4
Enabling search over encrypted cloud data is essential for privacy-preserving data outsourcing. While searchable encryption has evolved to support individual requirements like fuzz…
When Better Features Mean Greater Risks: The Performance-Privacy Trade-Off in Contrastive Learning
Ruining Sun, Hongsheng Hu, Wei Luo +4
With the rapid advancement of deep learning technology, pre-trained encoder models have demonstrated exceptional feature extraction capabilities, playing a pivotal role in the rese…
AGRAMPLIFIER: Defending Federated Learning Against Poisoning Attacks Through Local Update Amplification
Zirui Gong, Liyue Shen, Yanjun Zhang +4
The collaborative nature of federated learning (FL) poses a major threat in the form of manipulation of local training data and local updates, known as the Byzantine poisoning atta…
A Four-Pronged Defense Against Byzantine Attacks in Federated Learning
Wei Wan, Shengshan Hu, Minghui Li +4
\textit{Federated learning} (FL) is a nascent distributed learning paradigm to train a shared global model without violating users' privacy. FL has been shown to be vulnerable to v…