惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

博客园 - 聂微东
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
月光博客
月光博客
博客园 - 三生石上(FineUI控件)
The Cloudflare Blog
博客园 - Franky
IT之家
IT之家
V
Visual Studio Blog
博客园 - 【当耐特】
阮一峰的网络日志
阮一峰的网络日志
V
V2EX
钛媒体:引领未来商业与生活新知
钛媒体:引领未来商业与生活新知
博客园 - 司徒正美
爱范儿
爱范儿
Hugging Face - Blog
Hugging Face - Blog
宝玉的分享
宝玉的分享
博客园 - 叶小钗
有赞技术团队
有赞技术团队
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
酷 壳 – CoolShell
酷 壳 – CoolShell
量子位
罗磊的独立博客
小众软件
小众软件
Jina AI
Jina AI

math updates on arXiv.org

Coupling-Robust Accuracy in Multiphysics Physics Informed Neural Networks via Kronecker-Preconditioned Optimization Non-normal spectral signatures of instability in neural network training dynamics Optimization of randomized neural networks for transfer operator approximation Selective Ambulance Dispatch Under Contextual Travel-Time Uncertainty LLAMA LIMA: A Living Meta-Analysis on the Effects of Generative AI on Learning Mathematics Neural Flow Operators can Approximate any Operator: Abstract Frameworks and Universal Approximations LLMs as Noisy Channels: A Shannon Perspective on Model Capacity and Scaling Laws On the Stability of Spherical Hellinger-Kantorovich Flows and Their Implications for Differential Privacy Training-Free Looped Transformers Move on Muon : A Hamiltonian probability gradient flow perspective of Muon optimizer Entrywise Error Bounds for Spectral Ranking with Semi-Random Adversaries Asymmetric Scaling Laws from Sparse Features Is Dimensionality a Barrier for Retrieval Models? RA-DCA: A Randomized Active-Set DCA for Directional Stationarity in Max-Structured DC Programs Commutator-Induced Uncertainty in VAEs Weisfeiler-Leman Is Incomplete on Simple Spectrum Graphs, so Canonicalize Them Sparse In-Network Learning via Shortest-Path Backpropagation and Finite-Rate Gating Instance-Optimal Estimation with Multiple LLM Judges on a Budget Entropy Equivalence Testing Expand More, Shrink Less: Shaping Effective-Rank Dynamics for Dense Scaling in Recommendation Any-Dimensional Invariant Universality Operationalizing Individual Fairness via Gradient Descent and Bradley-Terry Models Anytime Training with Schedule-Free Spectral Optimization Diffusion-based Denoising Beats Vanilla Score Matching in Parameter Estimation: A Theoretical Explanation Resilience Characterization of AI-Native Wireless Receivers via Persistent Homology The General Theory of Localization Methods Group-Algebraic Tensors: Provably-optimal Equivariant Learning and Physical Symmetry Discovery General Lower Bounds for Differentially Private Federated Learning with Arbitrary Public-Transcript Interactions PilotWiMAE: Pilot-Native Representation Learning for Wireless Channels Proximal basin hopping: global optimization with guarantees
Secret-key-based physical layer security for feedback-aid...
Mohammad Javad Ahmadi, Rafael F. Schaefer, F. Rostami Ghadi, H. · 2025-12-10 · via math updates on arXiv.org

This work introduces security for unsourced random access (URA) via a physical-layer security approach. To achieve confidentiality, the proposed system opportunistically exploits intrinsic features of feedback-aided URA without altering its original structure or operational characteristics. As a result, the system preserves URA's efficiency, including low delay and minimal signaling overhead, while ensuring secure communication. To secure transmission, each user generates a secret key from a feedback signal broadcast by the BS in a previous transmission round, which depends on the BS-user channel and can thus be treated as private. Each user then encrypts its data using the secret key before transmission. Along with the encrypted data, only the parity bits of the LDPC-encoded key are transmitted, enabling secret key recovery at the legitimate receiver via Slepian-Wolf decoding with side information. We propose a receiver algorithm to recover both the encrypted data and the encoded secret key at the legitimate receiver. We further present a theoretical analysis to derive analytical error probabilities for both the legitimate receiver and the passive eavesdropper, as well as to quantify the additional load imposed by the security measures on the URA system. It is shown, based on both theoretical analysis and simulation results, that meaningful secrecy is achieved with only negligible extra overhead compared to the standard URA system.