Secure multi-party computation protocols are proposed for arbitrary computations on decentralized data markets, with reported performance on two healthcare applications.
Tokenized Data Markets
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abstract
We formalize the construction of decentralized data markets by introducing the mathematical construction of tokenized data structures, a new form of incentivized data structure. These structures both specialize and extend past work on token curated registries and distributed data structures. They provide a unified model for reasoning about complex data structures assembled by multiple agents with differing incentives. We introduce a number of examples of tokenized data structures and introduce a simple mathematical framework for analyzing their properties. We demonstrate how tokenized data structures can be used to instantiate a decentralized, tokenized data market, and conclude by discussing how such decentralized markets could prove fruitful for the further development of machine learning and AI.
fields
cs.CR 1years
2019 1verdicts
UNVERDICTED 1representative citing papers
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Secure Computation in Decentralized Data Markets
Secure multi-party computation protocols are proposed for arbitrary computations on decentralized data markets, with reported performance on two healthcare applications.