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Neural Fairness Blockchain Protocol Using an Elliptic Curves Lottery

Author

Listed:
  • Fabio Caldarola

    (Department of Mathematics and Computer Science, Cubo 31/A, Università della Calabria, 87036 Rende, Italy)

  • Gianfranco d’Atri

    (Department of Mathematics and Computer Science, Cubo 31/A, Università della Calabria, 87036 Rende, Italy)

  • Enrico Zanardo

    (Department of Digital Innovation, University of Nicosia, 46 Makedonitissas Avenue, CY-2417, Nicosia P.O. Box 24005, Cyprus)

Abstract

To protect participants’ confidentiality, blockchains can be outfitted with anonymization methods. Observations of the underlying network traffic can identify the author of a transaction request, although these mechanisms often only consider the abstraction layer of blockchains. Previous systems either give topological confidentiality that may be compromised by an attacker in control of a large number of nodes, or provide strong cryptographic confidentiality but are so inefficient as to be practically unusable. In addition, there is no flexible mechanism to swap confidentiality for efficiency in order to accommodate practical demands. We propose a novel approach, the neural fairness protocol, which is a blockchain-based distributed ledger secured using neural networks and machine learning algorithms, enabling permissionless participation in the process of transition validation while concurrently providing strong assurance about the correct functioning of the entire network. Using cryptography and a custom implementation of elliptic curves, the protocol is designed to ensure the confidentiality of each transaction phase and peer-to-peer data exchange.

Suggested Citation

  • Fabio Caldarola & Gianfranco d’Atri & Enrico Zanardo, 2022. "Neural Fairness Blockchain Protocol Using an Elliptic Curves Lottery," Mathematics, MDPI, vol. 10(17), pages 1-20, August.
  • Handle: RePEc:gam:jmathe:v:10:y:2022:i:17:p:3040-:d:895445
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    References listed on IDEAS

    as
    1. Daniel Adu-Gyamfi & Albert Kofi Kwansah Ansah & Gabriel Kofi Armah & Seth Alornyo & Dominic Kwasi Adom & Fengli Zhang, 2022. "Towards bitcoin transaction anonymity with recurrent attack prevention," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(4), pages 1-17, August.
    2. Huanliang Xiong & Muxi Chen & Canghai Wu & Yingding Zhao & Wenlong Yi, 2022. "Research on Progress of Blockchain Consensus Algorithm: A Review on Recent Progress of Blockchain Consensus Algorithms," Future Internet, MDPI, vol. 14(2), pages 1-24, January.
    3. Mhand Hifi & Nabil Otmani, 2012. "An algorithm for the disjunctively constrained knapsack problem," International Journal of Operational Research, Inderscience Enterprises Ltd, vol. 13(1), pages 22-43.
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    Cited by:

    1. Zhongchang Zhou & Fenggang Sun & Xiangyu Chen & Dongxu Zhang & Tianzhen Han & Peng Lan, 2023. "A Decentralized Federated Learning Based on Node Selection and Knowledge Distillation," Mathematics, MDPI, vol. 11(14), pages 1-15, July.

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