Privacy‐Preserving Blockchain Mining: A Proof‐of‐Useful‐Work Consensus Scheme

Turesson, Hjalmar K.; Laskowski, Marek; Roatiş, Alexandra; Kim, Henry M.

doi:10.2139/ssrn.3206258

Cited by 5 publications

(2 citation statements)

References 24 publications

(23 reference statements)

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“…On the other hand, the proof-of-useful-work mechanism for AI requires the secure provision of an ML model architecture, training data, and test data. Extant literature that describes such systems uses selected groups of participants and committees with reputation mechanisms to govern this data provision [26,67,78].…”

Section: ) Dlt Protocolmentioning

confidence: 99%

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On the Convergence of Artificial Intelligence and Distributed Ledger Technology: A Scoping Review and Future Research Agenda

et al. 2020

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Developments in Artificial Intelligence (AI) and Distributed Ledger Technology (DLT) currently lead to lively debates in academia and practice. AI processes data to perform tasks that were previously thought possible only for humans. DLT has the potential to create consensus over data among a group of participants in uncertain environments. In recent research, both technologies are used in similar and even the same systems. Examples include the design of secure distributed ledgers or the creation of allied learning systems distributed across multiple nodes. This can lead to technological convergence, which in the past, has paved the way for major innovations in information technology. Previous work highlights several potential benefits of the convergence of AI and DLT but only provides a limited theoretical framework to describe upcoming real-world integration cases of both technologies. We aim to contribute by conducting a systematic literature review on previous work and providing rigorously derived future research opportunities. This work helps researchers active in AI or DLT to overcome current limitations in their field, and practitioners to develop systems along with the convergence of both technologies.

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Section: ) Dlt Protocolmentioning

confidence: 99%

“…For such a system to work, however, several challenges have to be overcome. On the one hand, the computational difficulty of optimizing an ML model, is hard to adjust [78]. This can also cause blockchain forks to occur on a very regular basis.…”

Section: A Decentralized Computing For Aimentioning

confidence: 99%