ABSE
Adaptive Baseline Score-Based Election for Leader-Based BFT Systems
Xuyang Liu (The University of Auckland, School of Cyberspace Science and Technology, Beijing Institute of Technology)
Zijian Zhang (School of Cyberspace Science and Technology, Beijing Institute of Technology)
Zhen Li (School of Computer Science and Technology, Beijing Institute of Technology)
Hao Yin (Peking University)
Meng Li (Ministry of Education, Università degli Studi di Padova, Hefei University of Technology)
Jiamou Liu (The University of Auckland)
Mauro Conti (Università degli Studi di Padova, TU Delft - Electrical Engineering, Mathematics and Computer Science)
Liehuang Zhu (School of Cyberspace Science and Technology, Beijing Institute of Technology)
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Abstract
Leader-based BFT systems face potential disruption and performance degradation from malicious leaders, with current solutions often lacking scalability or greatly increasing complexity. In this paper, we introduce ABSE, an Adaptive Baseline Score-based Election approach to mitigate the negative impact of malicious leaders on leader-based BFT systems. ABSE is fully localized and proposes to accumulate scores for processes based on their contribution to consensus advancement, aiming to bypass less reliable participants when electing leaders. We present a formal treatment of ABSE, addressing the primary design and implementation challenges, defining its generic components and rules for adherence to ensure global consistency. We also apply ABSE to two different BFT protocols, demonstrating its scalability and negligible impact on protocol complexity. Finally, by building a system prototype and conducting experiments on it, we demonstrate that ABSE-enhanced protocols can effectively minimize the disruptions caused by malicious leaders, whilst incurring minimal additional resource overhead and maintaining base performance.