TY - GEN
T1 - MnemoSys
T2 - 4th IEEE International Conference on Trust, Privacy and Security in Intelligent Systems, and Applications, TPS-ISA 2022
AU - Rouhana, Daniel
AU - Lundquist, Peyton
AU - Andersen, Tim
AU - Dagher, Gaby G.
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - 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 behav-ior, 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.
AB - 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 behav-ior, 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.
KW - Blockchain
KW - Boltzmann
KW - Distributed Systems
KW - Quorum
KW - Reputation System
UR - http://www.scopus.com/inward/record.url?scp=85150678158&partnerID=8YFLogxK
U2 - 10.1109/TPS-ISA56441.2022.00019
DO - 10.1109/TPS-ISA56441.2022.00019
M3 - Conference contribution
AN - SCOPUS:85150678158
T3 - Proceedings - 2022 IEEE 4th International Conference on Trust, Privacy and Security in Intelligent Systems, and Applications, TPS-ISA 2022
SP - 67
EP - 76
BT - Proceedings - 2022 IEEE 4th International Conference on Trust, Privacy and Security in Intelligent Systems, and Applications, TPS-ISA 2022
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 14 December 2022 through 16 December 2022
ER -