Search Results for author: Rafael F. Schaefer

Found 16 papers, 1 papers with code

Secret Key Generation Rates for Line of Sight Multipath Channels in the Presence of Eavesdroppers

no code implementations25 Apr 2024 Amitha Mayya, Arsenia Chorti, Rafael F. Schaefer, Gerhard P. Fettweis

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.

Diffusion Models for Accurate Channel Distribution Generation

no code implementations19 Sep 2023 Muah Kim, Rick Fritschek, Rafael F. Schaefer

In this paper, we address this channel approximation challenge with diffusion models (DMs), which have demonstrated high sample quality and mode coverage in image generation.

Image Generation Scheduling

Generalized Rainbow Differential Privacy

no code implementations11 Sep 2023 Yuzhou Gu, Ziqi Zhou, Onur Günlü, Rafael G. L. D'Oliveira, Parastoo Sadeghi, Muriel Médard, Rafael F. Schaefer

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

no code implementations3 Feb 2023 Muah Kim, Rick Fritschek, Rafael F. Schaefer

This places the challenge to approximate or generate the channel or its derivative from samples generated by pilot signaling in real-world scenarios.

Generative Adversarial Network

Concatenated Classic and Neural (CCN) Codes: ConcatenatedAE

no code implementations4 Sep 2022 Onur Günlü, Rick Fritschek, Rafael F. Schaefer

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

no code implementations10 May 2022 Onur Günlü, Rafael F. Schaefer, Holger Boche, H. Vincent Poor

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

no code implementations22 Feb 2022 Onur Günlü, Matthieu Bloch, Rafael F. Schaefer, Aylin Yener

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

no code implementations8 Feb 2022 Ziqi Zhou, Onur Günlü, Rafael G. L. D'Oliveira, Muriel Médard, Parastoo Sadeghi, Rafael F. Schaefer

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

no code implementations12 Nov 2021 Gerhard Wunder, Benedikt Groß, Rick Fritschek, Rafael F. Schaefer

The Jensen inequality is a widely used tool in a multitude of fields, such as for example information theory and machine learning.

Mutual Information Estimation

Quality of Service Guarantees for Physical Unclonable Functions

no code implementations12 Jul 2021 Onur Günlü, Rafael F. Schaefer, H. Vincent Poor

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

no code implementations17 Jun 2021 Onur Günlü, Matthieu Bloch, Rafael F. Schaefer

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

no code implementations1 Jun 2021 Rick Fritschek, Rafael F. Schaefer, Gerhard Wunder

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.

Mutual Information Estimation reinforcement-learning +1

Federated Learning with Local Differential Privacy: Trade-offs between Privacy, Utility, and Communication

no code implementations9 Feb 2021 Muah Kim, Onur Günlü, Rafael F. Schaefer

We consider Gaussian mechanisms to preserve local differential privacy (LDP) of user data in the FL model with SGD.

Federated Learning

Secret Key Agreement with Physical Unclonable Functions: An Optimality Summary

no code implementations16 Dec 2020 Onur Günlü, Rafael F. Schaefer

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

no code implementations20 Feb 2020 Efe Bozkir, Onur Günlü, Wolfgang Fuhl, Rafael F. Schaefer, Enkelejda Kasneci

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.

General Classification

Deep Learning for Channel Coding via Neural Mutual Information Estimation

1 code implementation7 Mar 2019 Rick Fritschek, Rafael F. Schaefer, Gerhard Wunder

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.

Decoder Mutual Information Estimation

Cannot find the paper you are looking for? You can Submit a new open access paper.