Be Your Neighbor's Miner: Building Trust in Ledger Content via Reciprocally Useful Work

Distributed Ledgers (DLs) like Blockchain have become a popular technique to build collective trust in digital records. The rationale is that any agent wishing to append a block to a DL needs to provide proof of holding some property/asset or having performed some costly activity. Thus, “poisoning” a DL with spurious content requires much more effort than poisoning a conventional shared data structure. Based on this idea, DLs are now being deployed as community stores of trusted transaction records, reputation values and even of trustworthy training data for Machine Learning (ML) models. Certainly, when injecting spurious or hostile content in a DL, a rational attacker has to consider whether the damage δ caused by a spurious block B is worth the effort ε needed to append B to the DL; but practical experience has shown that being certain to disrupt a DL-supported application may be a powerful motivator for digital vandalism even when it is costly. In this paper, we put out an alternative idea: Reciprocally Useful Work (RUW), a novel DL update mechanism where any agent wishing to add a block B to the ledger must first perform an activity that will improve the utility for the DL-supported application of some other agent's block B
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. We discuss in detail how to apply RUW to DLs storing training data for Machine Learning (ML) models, in order to show that reciprocity can play the role of a direct compensation of the potential disruption, which is measurable in term of the performance of the ML model trained on the DL content.