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CoT: A Secure Consensus of Trust with Delegation Mechanism in Blockchains

  • Sai Lv
  • Hui LiEmail author
  • Han Wang
  • Xiangui Wang
Conference paper
  • 12 Downloads
Part of the Communications in Computer and Information Science book series (CCIS, volume 1176)

Abstract

The consensus algorithm is a key part of blockchains, which significantly influences the performance of security and efficiency. The PoW consensus guarantees the security of decentralized systems by competing to solve a puzzle, while with serious energy waste and low throughout. Follow-up consensus algorithms adopt delegation mechanisms to improve throughput and scalability. However, these delegation mechanisms, which are essentially partly decentralized, have security risks. This paper presents a consensus algorithm based on trust relationship between nodes, called Consensus of Trust (CoT), and introduces real-time credit of nodes into the delegation mechanism of the blockchain system. Firstly, CoT quantifies the trust relationship between nodes based on interactive transactions and generates the corresponding credit graph and matrix. It then uses the iterative algorithm, a variant of PageRank, to calculate the credit value of each node from the trust matrix. The nodes with high credit value are selected as the delegated nodes to participate in the block generation. We finally analyze the security performance that CoT can tolerant more than 33% of nodes to be malicious. We also prove the effectiveness and consistency in CoT.

Keywords

Blockchain Delegation mechanism Consensus algorithm Security 

Notes

Acknowledgement

This work is supported by the Natural Science Foundation of China (NSFC) (No. 61671001), GuangDong Prov., Key Program (No. 2019B010137001), PCL Future Regional Network Facilities for Large-scale Experiments and Applications (PCL2018KP001), National Keystone Program of China (No. 2017YFB0803204), Shenzhen Research Programs (JCYJ20170306092030521), and the Shenzhen Municipal Development and Reform Commission (Disciplinary Development Program for Data Science and Intelligent Computing).

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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Shenzhen Graduate SchoolPeking UniversityBeijingPeople’s Republic of China
  2. 2.Peng Cheng LaboratoryShenzhenPeople’s Republic of China

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