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

罗磊的独立博客
U
Unit 42
N
Netflix TechBlog - Medium
人人都是产品经理
人人都是产品经理
Hugging Face - Blog
Hugging Face - Blog
腾讯CDC
小众软件
小众软件
V
Visual Studio Blog
T
Tailwind CSS Blog
Engineering at Meta
Engineering at Meta
博客园 - 叶小钗
GbyAI
GbyAI
爱范儿
爱范儿
雷峰网
雷峰网
Microsoft Azure Blog
Microsoft Azure Blog
D
DataBreaches.Net
博客园_首页
D
Docker
A
About on SuperTechFans
G
Google Developers Blog
I
InfoQ
T
The Blog of Author Tim Ferriss
V
V2EX
博客园 - Franky

cs.IT updates on arXiv.org

Theoretical Limits of Language Model Alignment $f$-Divergence Regularized RLHF: Two Tales of Sampling and Unified Analyses A Unified Measure-Theoretic View of Diffusion, Score-Based, and Flow Matching Generative Models When Can Voting Help, Hurt, or Change Course? Exact Structure of Binary Test-Time Aggregation When Semantic Communication Meets Queueing: Cross-Layer Latency and Task Fidelity Optimization Convexity in Disguise: A Theoretical Framework for Nonconvex Low-Rank Matrix Estimation Conditional Diffusion Under Linear Constraints: Langevin Mixing and Information-Theoretic Guarantees Sharp Capacity Thresholds in Linear Associative Memory: From Winner-Take-All to Listwise Retrieval Expert Routing for Communication-Efficient MoE via Finite Expert Banks Contextual Memory-Enhanced Source Coding for Low-SNR Communications Realizable Bayes-Consistency for General Metric Losses Leveraging Code Automorphisms for Improved Syndrome-Based Neural Decoding A Hierarchical Sampling Framework for bounding the Generalization Error of Federated Learning Dueling DDQN-Based Adaptive Multi-Objective Handover Optimization for LEO Satellite Networks The Causal Description Gap: Information-Theoretic Separations Across Pearl's Hierarchy Optimization of CV-QKD Under Practical Constraints Benchmarking Wireless Representations: High-Dimensional vs. Compressed Embeddings for Efficiency and Robustness Real-Time Text Transmission via LLM-Based Entropy Coding over Fixed-Rate Channels SwiftChannel: Algorithm-Hardware Co-Design for Deep Learning-Based 5G Channel Estimation Evolving Token Communication with Parametric Memory Network Remote Action Generation: Remote Control with Minimal Communication The (Marginal) Value of a Search Ad: An Online Causal Framework for Repeated Second-price Auctions Stabilizing Private LASSO under Heterogeneous Covariates via Anisotropic Objective Perturbation Linear-Readout Floors and Threshold Recovery in Computation in Superposition Soft Graph Diffusion Transformer for MIMO Detection Hierarchical Federated Learning for Networked AI: From Communication Saving to Architecture-Aware Design Exponential families from a single KL identity MIFair: A Mutual-Information Framework for Intersectionality and Multiclass Fairness Diffusion-OAMP for Joint Image Compression and Wireless Transmission Decoupled Descent: Exact Test Error Tracking Via Approximate Message Passing
Physical Layer Key Generation for Secure Power Line Commu...
Federico Passerini, Andrea M. Tonello · 2018-09-25 · via cs.IT updates on arXiv.org

Leakage of information in power line communication networks is a threat to privacy and security both in smart grids and in-home applications. A way to enhance security is to encode the transmitted information with a secret key. Relying on the channel properties, it is possible to generate a common key at the two communication ends without transmitting it through the broadcast channel. Since the key is generated locally, it is intrinsically secure from a possible eavesdropper. Most of the existing physical layer key generation techniques have been developed for symmetric channels. However, the power line channel is in general not symmetric, but just reciprocal. Therefore, in this paper, we propose two novel methods that exploit the reciprocity of the power line channel to generate common information at the two intended users. This information is processed through different quantization techniques to generate secret keys. To assess the security of the generated keys, we analyze the spatial correlation of the power line channels and verify the low correlation of the possible eavesdropping channels. The two proposed methods are tested on a measurement dataset. The results show that the information leaked to possible eavesdroppers has very low correlation to any secret key.