If You Like Me, Please Don’t “Like” Me: Inferring Vendor Bitcoin Addresses From Positive Reviews

Bitcoin and similar cryptocurrencies are becoming increasingly popular as a payment method in both legitimate and illegitimate online markets. Such markets usually deploy a review system that allows users to rate their purchases and help others to determine reliable vendors. Consequently, vendors ar...

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Autores principales:	Schäfer Jochen, Müller Christian, Armknecht Frederik
Formato:	article
Lenguaje:	EN
Publicado:	Sciendo 2022
Materias:	bitcoin markets reviews identification Ethics BJ1-1725 Electronic computers. Computer science QA75.5-76.95
Acceso en línea:	https://doaj.org/article/dc7257b3102a4d578fbd47cf1775bb5a
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id	oai:doaj.org-article:dc7257b3102a4d578fbd47cf1775bb5a
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spelling	oai:doaj.org-article:dc7257b3102a4d578fbd47cf1775bb5a2021-12-05T14:11:10ZIf You Like Me, Please Don’t “Like” Me: Inferring Vendor Bitcoin Addresses From Positive Reviews2299-098410.2478/popets-2022-0022https://doaj.org/article/dc7257b3102a4d578fbd47cf1775bb5a2022-01-01T00:00:00Zhttps://doi.org/10.2478/popets-2022-0022https://doaj.org/toc/2299-0984Bitcoin and similar cryptocurrencies are becoming increasingly popular as a payment method in both legitimate and illegitimate online markets. Such markets usually deploy a review system that allows users to rate their purchases and help others to determine reliable vendors. Consequently, vendors are interested into accumulating as many positive reviews (likes) as possible and to make these public. However, we present an attack that exploits these publicly available information to identify cryptocurrency addresses potentially belonging to vendors. In its basic variant, it focuses on vendors that reuse their addresses. We also show an extended variant that copes with the case that addresses are used only once. We demonstrate the applicability of the attack by modeling Bitcoin transactions based on vendor reviews of two separate darknet markets and retrieve matching transactions from the blockchain. By doing so, we can identify Bitcoin addresses likely belonging to darknet market vendors.Schäfer JochenMüller ChristianArmknecht FrederikSciendoarticlebitcoinmarketsreviewsidentificationEthicsBJ1-1725Electronic computers. Computer scienceQA75.5-76.95ENProceedings on Privacy Enhancing Technologies, Vol 2022, Iss 1, Pp 440-459 (2022)
institution	DOAJ
collection	DOAJ
language	EN
topic	bitcoin markets reviews identification Ethics BJ1-1725 Electronic computers. Computer science QA75.5-76.95
spellingShingle	bitcoin markets reviews identification Ethics BJ1-1725 Electronic computers. Computer science QA75.5-76.95 Schäfer Jochen Müller Christian Armknecht Frederik If You Like Me, Please Don’t “Like” Me: Inferring Vendor Bitcoin Addresses From Positive Reviews
description	Bitcoin and similar cryptocurrencies are becoming increasingly popular as a payment method in both legitimate and illegitimate online markets. Such markets usually deploy a review system that allows users to rate their purchases and help others to determine reliable vendors. Consequently, vendors are interested into accumulating as many positive reviews (likes) as possible and to make these public. However, we present an attack that exploits these publicly available information to identify cryptocurrency addresses potentially belonging to vendors. In its basic variant, it focuses on vendors that reuse their addresses. We also show an extended variant that copes with the case that addresses are used only once. We demonstrate the applicability of the attack by modeling Bitcoin transactions based on vendor reviews of two separate darknet markets and retrieve matching transactions from the blockchain. By doing so, we can identify Bitcoin addresses likely belonging to darknet market vendors.
format	article
author	Schäfer Jochen Müller Christian Armknecht Frederik
author_facet	Schäfer Jochen Müller Christian Armknecht Frederik
author_sort	Schäfer Jochen
title	If You Like Me, Please Don’t “Like” Me: Inferring Vendor Bitcoin Addresses From Positive Reviews
title_short	If You Like Me, Please Don’t “Like” Me: Inferring Vendor Bitcoin Addresses From Positive Reviews
title_full	If You Like Me, Please Don’t “Like” Me: Inferring Vendor Bitcoin Addresses From Positive Reviews
title_fullStr	If You Like Me, Please Don’t “Like” Me: Inferring Vendor Bitcoin Addresses From Positive Reviews
title_full_unstemmed	If You Like Me, Please Don’t “Like” Me: Inferring Vendor Bitcoin Addresses From Positive Reviews
title_sort	if you like me, please don’t “like” me: inferring vendor bitcoin addresses from positive reviews
publisher	Sciendo
publishDate	2022
url	https://doaj.org/article/dc7257b3102a4d578fbd47cf1775bb5a
work_keys_str_mv	AT schaferjochen ifyoulikemepleasedontlikemeinferringvendorbitcoinaddressesfrompositivereviews AT mullerchristian ifyoulikemepleasedontlikemeinferringvendorbitcoinaddressesfrompositivereviews AT armknechtfrederik ifyoulikemepleasedontlikemeinferringvendorbitcoinaddressesfrompositivereviews
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If You Like Me, Please Don’t “Like” Me: Inferring Vendor Bitcoin Addresses From Positive Reviews

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