Statistical privacy-preserving message broadcast for peer-to-peer networks.

Privacy concerns are widely discussed in research and society in general. For the public infrastructure of financial blockchains, this discussion encompasses the privacy of the originator of a transaction broadcasted on the underlying peer-to-peer network. Adaptive diffusion is an approach to expose...

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Auteurs principaux:	David Mödinger, Jan-Hendrik Lorenz, Franz J Hauck
Format:	article
Langue:	EN
Publié:	Public Library of Science (PLoS) 2021
Sujets:	Medicine R Science Q
Accès en ligne:	https://doaj.org/article/b43f708b79b94dbfaf4f7a0f954d73a6
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id	oai:doaj.org-article:b43f708b79b94dbfaf4f7a0f954d73a6
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spelling	oai:doaj.org-article:b43f708b79b94dbfaf4f7a0f954d73a62021-11-25T06:19:15ZStatistical privacy-preserving message broadcast for peer-to-peer networks.1932-620310.1371/journal.pone.0251458https://doaj.org/article/b43f708b79b94dbfaf4f7a0f954d73a62021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0251458https://doaj.org/toc/1932-6203Privacy concerns are widely discussed in research and society in general. For the public infrastructure of financial blockchains, this discussion encompasses the privacy of the originator of a transaction broadcasted on the underlying peer-to-peer network. Adaptive diffusion is an approach to expose an alternative source of a message to attackers. However, this approach assumes an unsuitable attacker model and a non-realistic network model for current peer-to-peer networks on the Internet. We transform adaptive diffusion into a new statistical privacy-preserving broadcast protocol for realistic current networks. We model a class of unstructured peer-to-peer networks as organically growing graphs and provide models for other classes of such networks. We show that the distribution of shortest paths can be modelled using a normal distribution [Formula: see text]. We determine statistical estimators for μ, σ via multivariate models. The model behaves logarithmic over the number of nodes n and proportional to an inverse exponential over the number of added edges per node k. These results facilitate the computation of optimal forwarding probabilities during the dissemination phase for maximum privacy, with participants having only limited information about network topology.David MödingerJan-Hendrik LorenzFranz J HauckPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 5, p e0251458 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q David Mödinger Jan-Hendrik Lorenz Franz J Hauck Statistical privacy-preserving message broadcast for peer-to-peer networks.
description	Privacy concerns are widely discussed in research and society in general. For the public infrastructure of financial blockchains, this discussion encompasses the privacy of the originator of a transaction broadcasted on the underlying peer-to-peer network. Adaptive diffusion is an approach to expose an alternative source of a message to attackers. However, this approach assumes an unsuitable attacker model and a non-realistic network model for current peer-to-peer networks on the Internet. We transform adaptive diffusion into a new statistical privacy-preserving broadcast protocol for realistic current networks. We model a class of unstructured peer-to-peer networks as organically growing graphs and provide models for other classes of such networks. We show that the distribution of shortest paths can be modelled using a normal distribution [Formula: see text]. We determine statistical estimators for μ, σ via multivariate models. The model behaves logarithmic over the number of nodes n and proportional to an inverse exponential over the number of added edges per node k. These results facilitate the computation of optimal forwarding probabilities during the dissemination phase for maximum privacy, with participants having only limited information about network topology.
format	article
author	David Mödinger Jan-Hendrik Lorenz Franz J Hauck
author_facet	David Mödinger Jan-Hendrik Lorenz Franz J Hauck
author_sort	David Mödinger
title	Statistical privacy-preserving message broadcast for peer-to-peer networks.
title_short	Statistical privacy-preserving message broadcast for peer-to-peer networks.
title_full	Statistical privacy-preserving message broadcast for peer-to-peer networks.
title_fullStr	Statistical privacy-preserving message broadcast for peer-to-peer networks.
title_full_unstemmed	Statistical privacy-preserving message broadcast for peer-to-peer networks.
title_sort	statistical privacy-preserving message broadcast for peer-to-peer networks.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/b43f708b79b94dbfaf4f7a0f954d73a6
work_keys_str_mv	AT davidmodinger statisticalprivacypreservingmessagebroadcastforpeertopeernetworks AT janhendriklorenz statisticalprivacypreservingmessagebroadcastforpeertopeernetworks AT franzjhauck statisticalprivacypreservingmessagebroadcastforpeertopeernetworks
_version_	1718413946767015936

Statistical privacy-preserving message broadcast for peer-to-peer networks.

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