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BlockML: a useful proof of work system based on machine learning tasks

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Andrea MerlinaAuthors Info & Claims

Middleware '19: Proceedings of the 20th International Middleware Conference Doctoral Symposium

Pages 6 - 8

https://doi.org/10.1145/3366624.3368156

Published: 09 December 2019 Publication History

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Abstract

The computation required by permissionless blockchains to securely moderate the block proposal rate is wasteful both in terms of energy and computation. We present BlockML, an alternative system that replaces Bitcoin's crypto puzzle with the training of Machine Learning (ML) models, providing a useful side product to the consensus protocol of permissionless blockchains.

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Cambridge Centre for Alternative Finance. [n. d.]. Cambridge Bitcoin Electricity Consumption Index (CBECI). https://cbeci.org/comparisons/

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Changhao Chenli, Boyang Li, Yiyu Shi, and Taeho Jung. 2019. Energy-recycling Blockchain with Proof-of-Deep-Learning. In arXiv:1902.03912 [cs.CR].

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Cited By

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Ressi DRomanello RPiazza CRossi S(2024)AI-enhanced blockchain technology: A review of advancements and opportunitiesJournal of Network and Computer Applications10.1016/j.jnca.2024.103858(103858)Online publication date: Mar-2024
https://doi.org/10.1016/j.jnca.2024.103858
Vubangsi MNyuga GAl-Turjman F(2024)Exploring the Intersection of Artificial Intelligence and Blockchain Technology in Complex Systems: A Systematic ReviewThe Smart IoT Blueprint: Engineering a Connected Future10.1007/978-3-031-63103-0_25(235-245)Online publication date: 14-Aug-2024
https://doi.org/10.1007/978-3-031-63103-0_25
Platt MMcBurney P(2023)Sybil in the Haystack: A Comprehensive Review of Blockchain Consensus Mechanisms in Search of Strong Sybil Attack ResistanceAlgorithms10.3390/a1601003416:1(34)Online publication date: 6-Jan-2023
https://doi.org/10.3390/a16010034
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Index Terms

BlockML: a useful proof of work system based on machine learning tasks
1. Computing methodologies
  1. Artificial intelligence
    1. Distributed artificial intelligence
      1. Cooperation and coordination
  2. Machine learning
    1. Machine learning approaches
      1. Neural networks

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Published In

Middleware '19: Proceedings of the 20th International Middleware Conference Doctoral Symposium

December 2019

59 pages

ISBN:9781450370394

DOI:10.1145/3366624

Conference Chairs:
Faisal Nawab
University of California Santa Cruz
,
Etienne Riviere
UCLouvain, Belgium

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

In-Cooperation

USENIX Assoc: USENIX Assoc
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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 09 December 2019

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Author Tags

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Short-paper

Funding Sources

Norges Forskningsråd
Direktoratet for internasjonalisering og kvalitetsutvikling i høgare utdanning

Conference

Middleware '19

Sponsor:

Middleware '19: 20th International Middleware Conference

December 9 - 13, 2019

California, Davis

Acceptance Rates

Overall Acceptance Rate 203 of 948 submissions, 21%

Upcoming Conference

MIDDLEWARE '24

25th International Middleware Conference

December 2 - 6, 2024

Hong Kong , Hong Kong

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Cited By

View all

Ressi DRomanello RPiazza CRossi S(2024)AI-enhanced blockchain technology: A review of advancements and opportunitiesJournal of Network and Computer Applications10.1016/j.jnca.2024.103858(103858)Online publication date: Mar-2024
https://doi.org/10.1016/j.jnca.2024.103858
Vubangsi MNyuga GAl-Turjman F(2024)Exploring the Intersection of Artificial Intelligence and Blockchain Technology in Complex Systems: A Systematic ReviewThe Smart IoT Blueprint: Engineering a Connected Future10.1007/978-3-031-63103-0_25(235-245)Online publication date: 14-Aug-2024
https://doi.org/10.1007/978-3-031-63103-0_25
Platt MMcBurney P(2023)Sybil in the Haystack: A Comprehensive Review of Blockchain Consensus Mechanisms in Search of Strong Sybil Attack ResistanceAlgorithms10.3390/a1601003416:1(34)Online publication date: 6-Jan-2023
https://doi.org/10.3390/a16010034
Afaq YManocha A(2023)Blockchain and Deep Learning Integration for Various Application: A ReviewJournal of Computer Information Systems10.1080/08874417.2023.217333064:1(92-105)Online publication date: 8-Feb-2023
https://doi.org/10.1080/08874417.2023.2173330
Dragos C(2022)Proof of Useful Work Based on Matrix Computation2022 24th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)10.1109/SYNASC57785.2022.00026(108-116)Online publication date: Sep-2022
https://doi.org/10.1109/SYNASC57785.2022.00026
Haouari MMhiri MEl-Masri MAl-Yafi K(2022)A novel proof of useful work for a blockchain storing transportation transactionsInformation Processing and Management: an International Journal10.1016/j.ipm.2021.10274959:1Online publication date: 9-Apr-2022
https://dl.acm.org/doi/10.1016/j.ipm.2021.102749
Shafay MAhmad RSalah KYaqoob IJayaraman ROmar M(2022)Blockchain for deep learning: review and open challengesCluster Computing10.1007/s10586-022-03582-726:1(197-221)Online publication date: 14-Mar-2022
https://doi.org/10.1007/s10586-022-03582-7
Yamina KYacine CLeila H(2021)ClusterChain: Decentralized and Trustworthy Clustering Over Blockchain2021 International Conference on Networking and Advanced Systems (ICNAS)10.1109/ICNAS53565.2021.9628967(1-6)Online publication date: 27-Oct-2021
https://doi.org/10.1109/ICNAS53565.2021.9628967
Mauri LDamiani ECimato S(2020)Be Your Neighbor's Miner: Building Trust in Ledger Content via Reciprocally Useful Work2020 IEEE 13th International Conference on Cloud Computing (CLOUD)10.1109/CLOUD49709.2020.00021(53-62)Online publication date: Oct-2020
https://doi.org/10.1109/CLOUD49709.2020.00021

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