We propose a data-driven secure wireless communication scheme, in which the goal is to transmit a signal to a legitimate receiver with minimal distortion, while keeping some information about the signal private from an eavesdropping adversary. When the data distribution is known, the optimal trade-off between the reconstruction quality at the legitimate receiver and the leakage to the adversary can be characterised in the information theoretic asymptotic limit. In this paper, we assume that we do not know the data distribution, but instead have access to a dataset, and we are interested in the finite blocklength regime rather than the asymptotic limits. We propose a data-driven adversarially trained deep joint source-channel coding architecture, and demonstrate through experiments with CIFAR-10 dataset that it is possible to transmit to the legitimate receiver with minimal end-to-end distortion while concealing information on the image class from the adversary.

Adversarial Networks for Secure Wireless Communications / Marchioro, T.; Laurenti, N.; Gunduz, D.. - 2020-:(2020), pp. 8748-8752. (Intervento presentato al convegno 2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 tenutosi a esp nel 2020) [10.1109/ICASSP40776.2020.9053216].

Adversarial Networks for Secure Wireless Communications

Marchioro T.;Gunduz D.
2020

Abstract

We propose a data-driven secure wireless communication scheme, in which the goal is to transmit a signal to a legitimate receiver with minimal distortion, while keeping some information about the signal private from an eavesdropping adversary. When the data distribution is known, the optimal trade-off between the reconstruction quality at the legitimate receiver and the leakage to the adversary can be characterised in the information theoretic asymptotic limit. In this paper, we assume that we do not know the data distribution, but instead have access to a dataset, and we are interested in the finite blocklength regime rather than the asymptotic limits. We propose a data-driven adversarially trained deep joint source-channel coding architecture, and demonstrate through experiments with CIFAR-10 dataset that it is possible to transmit to the legitimate receiver with minimal end-to-end distortion while concealing information on the image class from the adversary.
2020
2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020
esp
2020
2020-
8748
8752
Marchioro, T.; Laurenti, N.; Gunduz, D.
Adversarial Networks for Secure Wireless Communications / Marchioro, T.; Laurenti, N.; Gunduz, D.. - 2020-:(2020), pp. 8748-8752. (Intervento presentato al convegno 2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 tenutosi a esp nel 2020) [10.1109/ICASSP40776.2020.9053216].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11380/1247346
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