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APPLICATION OF STATISTICAL MODELING AND PARTICIPATORY COMPUTING FOR PRIVATE BLOCKCHAINS
Abstract
With the steady rise of interest in blockchain and related technologies, researchers are making efforts to solve the remaining open questions in this field. One of the open problems is to achieve and maintain some desired characteristics of a private blockchain as far as the block generation rate is concerned. This problem is routed in statistical modeling. In this work, we will apply the results of participatory computing to build private blockchains and to resolve this issue. Private blockchains are important in cases when enforcing limited write access, while maintaining public readability and undeniability. One example could be the collection of publicly important data (e.g. environmental information, agricultural data) from a set of trusted sources (i.e. research stations). By applying participatory systems to blockchain technologies, there are a fixed number of participants in a group, who are in need for a private blockchain. Each participant has a number of clients, whom they provide service for. The participants use the resources of their clients to "build" the blockchain, i.e. the clients provide the proof-of-work required to append new blocks to the chain. In this paper we provide a mathematical model of this process and by statistical analysis we demonstrate how to achieve the desired block generation rate by evaluating its p.d.f. Our approach is based on modeling the random behavior of blockchains as well as the typical client behavior.
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