20 citations · 46 across the 16 of their papers we have counts for
16 papers
Physical layer insecurity
Muriel Médard, Ken R. Duffy
In the classic wiretap model, Alice wishes to reliably communicate to Bob without being overheard by Eve who is eavesdropping over a degraded channel. Systems for achieving that ph…
GRAND-assisted Optimal Modulation
Basak Ozaydin, Muriel Médard, Ken Duffy
Optimal modulation (OM) schemes for Gaussian channels with peak and average power constraints are known to require nonuniform probability distributions over signal points, which pr…
A Bivariate Invariance Principle
Alexander Mariona, Homa Esfahanizadeh, Rafael G. L. D'Oliveira +1
A notable result from analysis of Boolean functions is the Basic Invariance Principle (BIP), a quantitative nonlinear generalization of the Central Limit Theorem for multilinear po…
FlEC: Enhancing QUIC with application-tailored reliability mechanisms
François Michel, Alejandro Cohen, Derya Malak +3
Packet losses are common events in today's networks. They usually result in longer delivery times for application data since retransmissions are the de facto technique to recover f…
Absolute Security in High-Frequency Wireless Links
Alejandro Cohen, Rafael G. L. D'Oliveira, Chia-Yi Yeh +6
Security against eavesdropping is one of the key concerns in the design of any communication system. Many common considerations of the security of a wireless communication channel…
Block turbo decoding with ORBGRAND
Kevin Galligan, Muriel Médard, Ken R. Duffy
Guessing Random Additive Noise Decoding (GRAND) is a family of universal decoding algorithms suitable for decoding any moderate redundancy code of any length. We establish that, th…
Distributed Computations with Layered Resolution
Homa Esfahanizadeh, Alejandro Cohen, Muriel Médard +1
Modern computationally-heavy applications are often time-sensitive, demanding distributed strategies to accelerate them. On the other hand, distributed computing suffers from the b…
GRAND for Fading Channels using Pseudo-soft Information
Hadi Sarieddeen, Muriel Médard, Ken. R. Duffy
Guessing random additive noise decoding (GRAND) is a universal maximum-likelihood decoder that recovers code-words by guessing rank-ordered putative noise sequences and inverting t…
Soft-input, soft-output joint detection and GRAND
Hadi Sarieddeen, Muriel Médard, Ken. R. Duffy
Guessing random additive noise decoding (GRAND) is a maximum likelihood (ML) decoding method that identifies the noise effects corrupting code-words of arbitrary code-books. In a j…
Ultra-Reliable Low-Latency Millimeter-Wave Communications with Sliding Window Network Coding
Eurico Dias, Duarte Raposo, Homa Esfahanizadeh +5
Ultra-reliability and low-latency are pivotal requirements of the new 6th generation of communication systems (xURLLC). Over the past years, to increase throughput, adaptive active…
On the Role of Quantization of Soft Information in GRAND
Peihong Yuan, Ken R. Duffy, Evan P. Gabhart +1
In this work, we investigate guessing random additive noise decoding (GRAND) with quantized soft input. First, we analyze the achievable rate of ordered reliability bits GRAND (ORB…
AES as Error Correction: Cryptosystems for Reliable Communication
Alejandro Cohen, Rafael G. L. D'Oliveira, Ken R. Duffy +2
In this paper, we show that the Advanced Encryption Standard (AES) cryptosystem can be used as an error-correcting code to obtain reliability over noisy communication and data syst…
Broadcast Approach Meets Network Coding for Data Streaming
Alejandro Cohen, Muriel Médard, Shlomo Shamai
For data streaming applications, existing solutions are not yet able to close the gap between high data rates and low delay. This work considers the problem of data streaming under…
Partial Encryption after Encoding for Security and Reliability in Data Systems
Alejandro Cohen, Rafael G. L. D'Oliveira, Ken R. Duffy +1
We consider the problem of secure and reliable communication over a noisy multipath network. Previous work considering a noiseless version of our problem proposed a hybrid universa…
Rainbow Differential Privacy
Ziqi Zhou, Onur Günlü, Rafael G. L. D'Oliveira +3
We extend a previous framework for designing differentially private (DP) mechanisms via randomized graph colorings that was restricted to binary functions, corresponding to colorin…
Syfer: Neural Obfuscation for Private Data Release
Adam Yala, Victor Quach, Homa Esfahanizadeh +5
Balancing privacy and predictive utility remains a central challenge for machine learning in healthcare. In this paper, we develop Syfer, a neural obfuscation method to protect aga…