Analysis of multi‐input multi‐output transactions in the Bitcoin network. (19th December 2019)

• Record Type: Journal Article
• Authors: Phetsouvanh, Silivanxay; Datta, Anwitaman; Oggier, Frédérique
• Other Names: Schifanella Claudio guestEditor.; Bracciali Andrea guestEditor.; Benkrid Soumia guestEditor.; Moussa Rim guestEditor.; Badir Hassan guestEditor.; Lo Moussa guestEditor.; Bellatreche Ladjel guestEditor.
• Is Part Of: Concurrency and computation. Volume 33:Number 1(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2019-12-19
• Subjects: Bitcoin network -- exploratory data analysis -- graph analysis -- mixing; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.5629
• Languages: English
• ISSNs: 1532-0626

Abstract

Summary: Distinct transactions among different and unrelated users are combined together to create a single Bitcoin transaction (mixing transaction) to obfuscate the relationships among the actual participants (more specifically, the wallet addresses used for the transactions). We consider multi‐input multi‐output transactions with at least two inputs and three outputs as proxy, to analyze four characteristic periods of ∼50 days each, representing periods before the introduction of mixing, in its early days, during its growth, and after the volume of such multi‐input multi‐output transactions became more or less stabile. Structural properties and characteristics of the transaction and wallet address networks are computed and compared, through standard tools, but also via the introduction of two novel techniques that provide indicators of mixing‐like behaviors: (1) an entropy characterization to detect abnormally uniform inputs and/or outputs and (2) a connected component analysis of subgraphs formed by only multi‐input multi‐output transactions (showing cascades of such transactions). The contributions of this exploratory Bitcoin network analysis paper can thus be seen as two‐fold. At a macroscopic level, the growth and stabilization periods are shown to stand out with respect to most considered metrics, while at a microscopic level, chains of multi‐input multi‐output transactions, and transactions with outlier behavior in terms of input/output entropies are identified for further investigation.