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Found 16 papers, 1 papers with code
Secret Key Generation Rates for Line of Sight Multipath Channels in the Presence of Eavesdroppers
Our investigation centers on a frequency-selective line-of-sight (LoS) multipath channel, with a particular emphasis on assessing SKG rates derived from the distributions of RSS.
Learning End-to-End Channel Coding with Diffusion Models
Furthermore, we examine the trade-off between sample quality and sampling speed, when an accelerated sampling algorithm is deployed, and investigate the effect of the noise scheduling on this trade-off.
Generalized Rainbow Differential Privacy
In this framework, datasets are nodes in a graph, and two neighboring datasets are connected by an edge.
Learning End-to-End Channel Coding with Diffusion Models
This places the challenge to approximate or generate the channel or its derivative from samples generated by pilot signaling in real-world scenarios.
Concatenated Classic and Neural (CCN) Codes: ConcatenatedAE
Small neural networks (NNs) used for error correction were shown to improve on classic channel codes and to address channel model changes.
Secure and Private Source Coding with Private Key and Decoder Side Information
The problem of secure source coding with multiple terminals is extended by considering a remote source whose noisy measurements are the correlated random variables used for secure source reconstruction.
Secure Joint Communication and Sensing
For independent and identically distributed states, perfect output feedback, and when part of the transmitted message should be kept secret, a partial characterization of the secrecy-distortion region is developed.
Rainbow Differential Privacy
We extend a previous framework for designing differentially private (DP) mechanisms via randomized graph colorings that was restricted to binary functions, corresponding to colorings in a graph, to multi-valued functions.
A Reverse Jensen Inequality Result with Application to Mutual Information Estimation
The Jensen inequality is a widely used tool in a multitude of fields, such as for example information theory and machine learning.
Quality of Service Guarantees for Physical Unclonable Functions
A public ring oscillator (RO) output dataset is used to illustrate that a truncated Gaussian distribution can be fitted to transformed RO outputs that are inputs to uniform scalar quantizers such that reliability guarantees can be provided for each bit extracted from any PUF device under additive Gaussian noise components by eliminating a small subset of PUF outputs.
Secure Multi-Function Computation with Private Remote Sources
We consider a distributed function computation problem in which parties observing noisy versions of a remote source facilitate the computation of a function of their observations at a fusion center through public communication.
Reinforce Security: A Model-Free Approach Towards Secure Wiretap Coding
The use of deep learning-based techniques for approximating secure encoding functions has attracted considerable interest in wireless communications due to impressive results obtained for general coding and decoding tasks for wireless communication systems.
Federated Learning with Local Differential Privacy: Trade-offs between Privacy, Utility, and Communication
We consider Gaussian mechanisms to preserve local differential privacy (LDP) of user data in the FL model with SGD.
Secret Key Agreement with Physical Unclonable Functions: An Optimality Summary
A physical unclonable function (PUF) is a promising solution for local security in digital devices and this review gives the most relevant summary for information theorists, coding theorists, and signal processing community members who are interested in optimal PUF constructions.
Differential Privacy for Eye Tracking with Temporal Correlations
New generation head-mounted displays, such as VR and AR glasses, are coming into the market with already integrated eye tracking and are expected to enable novel ways of human-computer interaction in numerous applications.
Deep Learning for Channel Coding via Neural Mutual Information Estimation
However, one of the drawbacks of current learning approaches is that a differentiable channel model is needed for the training of the underlying neural networks.
