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Contributors: Please consider adding entries to this annotated bibliography (AB) as you read and research articles. This AB will serve as a reference for papers and presentations we collaborate on together and as individuals. APA style.
Annotations
Wang, Y., Bracciali, A., Li, T., Li, F., Cui, X., & Zhao, M. (2019) Randomness invalidates criminal smart contracts. Information Sciences, 477, 291-301. https://doi.org/10.1016/j.ins.2018.10.057
This paper discusses the use of random factors based on PublicLeaks to minimize the ability of those using smart contracts for criminal activities. While it talks of the mitigation of criminal acts via smart contracts it does not give guarantees, but instead looks at them from a risk management viewpoint.
Much of the paper works upon the idea of entering additional factors such randomness. While this may prove somewhat effective the statement of, “As with real-world crimes, CSCs are not as powerful as assumed.” Is not an overly realistic view. Another statement concerning machine learning as an additional method for ferreting out criminal activities and disallowing their usage of the system is actually a far more effective and realistic method for control.
This source has helped me with a better understanding of both sides concerning both legal and illegal applications of smart contracts. While the authors believe in the usage of smart contracts as a way to improve trust in blockchain transactions via adding randomness, they also feel more must be done to ensure their legitimate non-criminal usage.
Allen, D., Berg, C., Davidson, S., Noval, M., & Potts, J. (2019, May). International policy coordination for blockchain supply chains. Asia & the Pacific Policy Studies, https://doi.org/10.1002/app5.281 | Full text
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