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Appears in Collections:Computing Science and Mathematics Journal Articles
Peer Review Status: Refereed
Title: Adversarial behaviours in mixing coins under incomplete information
Author(s): Wang, Yilei
Bracciali, Andrea
Yang, Guoyu
Li, Tao
Yu, Xiaomei
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Keywords: Mixing coins
Incomplete information
Smart contract
Issue Date: 2020
Date Deposited: 18-Sep-2020
Citation: Wang Y, Bracciali A, Yang G, Li T & Yu X (2020) Adversarial behaviours in mixing coins under incomplete information. Applied Soft Computing, 96, Art. No.: 106605.
Abstract: Criminals can launder crypto-currencies through mixing coins, whose original purpose is preservation of privacy in the presence of traceability. Therefore, it is essential to elaborately design mixing polices to achieve both privacy and anti-money laundering. Existing work on mixing policies relies on the knowledge of a blacklist. However, these policies are paralysed under the scenario where the blacklist is unknown or evolving. In this paper, we regard the above scenario as games under incomplete information where parties put down a deposit for the quality of coins, which is suitably managed by a smart contract in case of mixing bad coins. We extend the poison and haircut policies to incomplete information games, where the blacklist is updated after mixing. We prove the existence of equilibria for the improved polices, while it is known that there is no equilibria in the original poison and haircut policies, where blacklist is public known. Furthermore, we propose a seminal suicide policy: the one who mixes more bad coins will be punished by not having the deposit refunded. Thus, parties have no incentives to launder money by leveraging mixing coins. In effect, all three policies contrast money laundering while preserving privacy under incomplete information. Finally, we simulate and verify the validity of these policies.
DOI Link: 10.1016/j.asoc.2020.106605
Rights: This item has been embargoed for a period. During the embargo please use the Request a Copy feature at the foot of the Repository record to request a copy directly from the author. You can only request a copy if you wish to use this work for your own research or private study. Accepted refereed manuscript of: Wang Y, Bracciali A, Yang G, Li T & Yu X (2020) Adversarial behaviours in mixing coins under incomplete information. Applied Soft Computing, 96, Art. No.: 106605. © 2020, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
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