Decentralized Zero-Trust Framework for Digital Twin-based 6G. (arXiv:2302.03107v1 [cs.CY])
The Sixth Generation (6G) network is a platform for the fusion of the physical and virtual worlds. It will integrate processing, communication, intelligence, sensing, and storage of things. All devices…
One-shot Empirical Privacy Estimation for Federated Learning. (arXiv:2302.03098v1 [cs.LG])
Privacy auditing techniques for differentially private (DP) algorithms are useful for estimating the privacy loss to compare against analytical bounds, or empirically measure privacy in settings where known analytical bounds…
Decentralized Zero-Trust Framework for Digital Twin-based 6G. (arXiv:2302.03107v1 [cs.CY])
The Sixth Generation (6G) network is a platform for the fusion of the physical and virtual worlds. It will integrate processing, communication, intelligence, sensing, and storage of things. All devices…
From Emulation to Mathematical: A More General Traffic Obfuscation Approach To Encounter Feature based Mobile App traffic Classification. (arXiv:2302.03118v1 [cs.CR])
The usage of the mobile app is unassailable in this digital era. While tons of data are generated daily, user privacy security concerns become an important issue. Nowadays, tons of…
Decentralized Zero-Trust Framework for Digital Twin-based 6G. (arXiv:2302.03107v1 [cs.CY])
The Sixth Generation (6G) network is a platform for the fusion of the physical and virtual worlds. It will integrate processing, communication, intelligence, sensing, and storage of things. All devices…
From Emulation to Mathematical: A More General Traffic Obfuscation Approach To Encounter Feature based Mobile App traffic Classification. (arXiv:2302.03118v1 [cs.CR])
The usage of the mobile app is unassailable in this digital era. While tons of data are generated daily, user privacy security concerns become an important issue. Nowadays, tons of…
From Emulation to Mathematical: A More General Traffic Obfuscation Approach To Encounter Feature based Mobile App traffic Classification. (arXiv:2302.03118v1 [cs.CR])
The usage of the mobile app is unassailable in this digital era. While tons of data are generated daily, user privacy security concerns become an important issue. Nowadays, tons of…
Protecting Language Generation Models via Invisible Watermarking. (arXiv:2302.03162v1 [cs.CR])
Language generation models have been an increasingly powerful enabler for many applications. Many such models offer free or affordable API access, which makes them potentially vulnerable to model extraction attacks…
Protecting Language Generation Models via Invisible Watermarking. (arXiv:2302.03162v1 [cs.CR])
Language generation models have been an increasingly powerful enabler for many applications. Many such models offer free or affordable API access, which makes them potentially vulnerable to model extraction attacks…
SCALE-UP: An Efficient Black-box Input-level Backdoor Detection via Analyzing Scaled Prediction Consistency. (arXiv:2302.03251v1 [cs.CR])
Deep neural networks (DNNs) are vulnerable to backdoor attacks, where adversaries embed a hidden backdoor trigger during the training process for malicious prediction manipulation. These attacks pose great threats to…