Reputation-based PoS for the Restriction of Illicit Activities on Blockchain: Algorand Usecase
This addresses the issue of criminal exploitation in blockchain networks for users and regulators, but it is incremental as it builds on existing detection methods without a new paradigm.
The paper tackles the problem of illicit activities like money laundering on permissionless blockchains by proposing a reputation-based method integrated into the consensus protocol of Algorand, which theoretically restricts criminal elements through block proposal rejection and reduced voting power without straining communication resources.
In cryptocurrency-based permissionless blockchain networks, the decentralized structure enables any user to join and operate across different regions. The criminal entities exploit it by using cryptocurrency transactions on the blockchain to facilitate activities such as money laundering, gambling, and ransomware attacks. In recent times, different machine learning-based techniques can detect such criminal elements based on blockchain transaction data. However, there is no provision within the blockchain to deal with such elements. We propose a reputation-based methodology for response to the users detected carrying out the aforementioned illicit activities. We select Algorand blockchain to implement our methodology by incorporating it within the consensus protocol. The theoretical results obtained prove the restriction and exclusion of criminal elements through block proposal rejection and attenuation of the voting power as a validator for such entities. Further, we analyze the efficacy of our method and show that it puts no additional strain on the communication resources.