We study privacy-aware communication over a wiretap channel using end-to-end learning. Alice wants to transmit a source signal to Bob over a binary symmetric channel, while passive eavesdropper Eve tries to infer some sensitive attribute of Alice's source based on its overheard signal. Since we usually do not have access to true distributions, we propose a data-driven approach using variational autoencoder (VAE)-based joint source channel coding (JSCC). We show through simulations with the colored MNIST dataset that our approach provides high reconstruction quality at the receiver while confusing the eavesdropper about the latent sensitive attribute, which consists of the color and thickness of the digits. Finally, we consider a parallel-channel scenario, and show that our approach arranges the information transmission such that the channels with higher noise levels at the eavesdropper carry the sensitive information, while the non-sensitive information is transmitted over more vulnerable channels.

PRIVACY-AWARE COMMUNICATION OVER A WIRETAP CHANNEL WITH GENERATIVE NETWORKS / Erdemir, E.; Dragotti, P. L.; Gunduz, D.. - 2022-:(2022), pp. 2989-2993. ((Intervento presentato al convegno 47th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022 tenutosi a Marina Bay Sands Expo and Convention Center, sgp nel 2022 [10.1109/ICASSP43922.2022.9747068].

PRIVACY-AWARE COMMUNICATION OVER A WIRETAP CHANNEL WITH GENERATIVE NETWORKS

Gunduz D.
2022

Abstract

We study privacy-aware communication over a wiretap channel using end-to-end learning. Alice wants to transmit a source signal to Bob over a binary symmetric channel, while passive eavesdropper Eve tries to infer some sensitive attribute of Alice's source based on its overheard signal. Since we usually do not have access to true distributions, we propose a data-driven approach using variational autoencoder (VAE)-based joint source channel coding (JSCC). We show through simulations with the colored MNIST dataset that our approach provides high reconstruction quality at the receiver while confusing the eavesdropper about the latent sensitive attribute, which consists of the color and thickness of the digits. Finally, we consider a parallel-channel scenario, and show that our approach arranges the information transmission such that the channels with higher noise levels at the eavesdropper carry the sensitive information, while the non-sensitive information is transmitted over more vulnerable channels.
47th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022
Marina Bay Sands Expo and Convention Center, sgp
2022
2022-
2989
2993
Erdemir, E.; Dragotti, P. L.; Gunduz, D.
PRIVACY-AWARE COMMUNICATION OVER A WIRETAP CHANNEL WITH GENERATIVE NETWORKS / Erdemir, E.; Dragotti, P. L.; Gunduz, D.. - 2022-:(2022), pp. 2989-2993. ((Intervento presentato al convegno 47th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022 tenutosi a Marina Bay Sands Expo and Convention Center, sgp nel 2022 [10.1109/ICASSP43922.2022.9747068].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11380/1286024
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