Randomized Distributed Function Computation with Semantic Communications: Applications to Privacy

Randomized distributed function computation refers to remote function computation where transmitters send datato receivers which compute function outputs that are randomized functions of the inputs. We study the applications of semantic communications in randomized distributed function computation t...

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Vydané v:2024 IEEE INTERNATIONAL WORKSHOP ON INFORMATION FORENSICS AND SECURITY, WIFS 2024 s. 1 - 6
Hlavný autor: Gunlu, Onur
Médium: Konferenčný príspevok..
Jazyk:English
Vydavateľské údaje: IEEE 02.12.2024
Edícia:IEEE International Workshop on Information Forensics and Security
Predmet:
efficient simulation of noisy channels over digital channels
Lower bound
Noise measurement
Privacy
Probability distribution
Random variables
Receivers
Security
Semantic communication
semantic communications for randomized distributed function computation
Source coding
Strong coordination
Transmitters
Strong coordination
semantic communications for randomized distributed function computation
efficient simulation of noisy channels over digital channels
ISBN:9798350364439, 9798350364422
ISSN:2157-4774
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Shrnutí:Randomized distributed function computation refers to remote function computation where transmitters send datato receivers which compute function outputs that are randomized functions of the inputs. We study the applications of semantic communications in randomized distributed function computation to illustrate significant reductions in the communication load, with a particular focus on privacy. The semantic communication framework leverages generalized remote source coding methods, where the remote source is a randomized version of the observed data. Since satisfying security and privacy constraints generally require a randomization step, semantic communication methods can be applied to such function computation problems, where the goal is to remotely simulate a sequence at the receiver such that the transmitter and receiver sequences follow a target probability distribution. Our performance metrics guarantee (local differential) privacy for each input sequence, used in two different distributed function computation problems, which is possible by using strong coordination methods even without common randomness.This work provides lower bounds on Wyner's common information (WCI), which is one of the two corner points of the coordination-randomness rate region characterizing the ultimate limits of randomized distributed function computation. The WCI corresponds to the case when there is no common randomness shared by the transmitter and receiver. Moreover, numerical methods are proposed to compute the other corner point for continuous-valued random variables, for which an unlimited amount of common randomness is available. Results for two problems of practical interest illustrate that leveraging common randomness can decrease the communication load as compared to the WCI corner point significantly. We also illustrate that semantic communication gains over lossless compression methods are achieved also without common randomness, motivating further research on limited common randomness scenarios.
ISBN:9798350364439
9798350364422
ISSN:2157-4774
DOI:10.1109/WIFS61860.2024.10810724