MnemoSys: A Conditional Probability Estimation Protocol for Blockchain Audited Reputation Management

【Author】 Rouhana, Daniel; Lundquist, Peyton; Andersen, Tim; Dagher, Gaby G.

【Source】2022 IEEE 4TH INTERNATIONAL CONFERENCE ON TRUST, PRIVACY AND SECURITY IN INTELLIGENT SYSTEMS, AND APPLICATIONS, TPS-ISA

【影响因子】

【Abstract】Reputation systems have been one method of solving the unique challenges that face distributed networks of independent operators. Fundamentally, historical performance must be considered in a way that attempts to predict future behavior, optimize present functionality, and provide some measure of immutable recording. In this paper, a three-part system, MnemoSys, is proposed to solve this diverse set of problems. First, historical performance is dynamically weighted and scored using geometrically expanding time windows. Second, a quorum is abstracted as a restricted Boltzmann machine to produce a conditional probability estimate of log-normal likelihood of good-faith behavior. Third, all rewards and punishments are recorded on an immutable, decentralized ledger. Our experimentation shows that when applied iteratively to an entire network, consistently under-performing nodes are removed, network stability is maintained even with high percentages of simulated error, and global network parameters are optimized in the long-term.

【Keywords】Blockchain; Reputation System; Boltzmann; Quorum; Distributed Systems

【发表时间】2022

【收录时间】2023-07-02

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