A Distributed and Privacy-Aware High-Throughput Transaction Scheduling Approach for Scaling Blockchain

• Published in: IEEE transactions on dependable and secure computing Vol. 20; no. 5; pp. 1 - 15
• Main Authors: Qiu, Xiaoyu, Chen, Wuhui, Tang, Bingxin, Liang, Junyuan, Dai, Hong-Ning, Zheng, Zibin
• Format: Journal Article
• Language: English
• Published: Washington IEEE 01-09-2023; IEEE Computer Society
• Subjects: Algorithms; Blockchain; Blockchains; Constraints; Cryptography; Deep learning; deep reinforcement learning; distributed training; Heuristic algorithms; Markov processes; Network design; Optimization; Privacy; privacy-aware; Routing; Schedules; Scheduling; Throughput; Training; transaction scheduling
• ISSN: 1545-5971; 1941-0018
• DOI: 10.1109/TDSC.2022.3216571

Payment channel networks (PCNs) are considered as a prominent solution for scaling blockchain, where users can establish payment channels and complete transactions in an off-chain manner. However, it is non-trivial to schedule transactions in PCNs and most existing routing algorithms suffer from the following challenges: 1) one-shot optimization, 2) privacy-invasive channel probing, 3) vulnerability to DoS attacks. To address these challenges, we propose a privacy-aware transaction scheduling algorithm with defence against DoS attacks based on deep reinforcement learning (DRL), namely PTRD. Specifically, considering both the privacy preservation and long-term throughput into the optimization criteria, we formulate the transaction-scheduling problem as a Constrained Markov Decision Process. We then design PTRD, which extends off-the-shelf DRL algorithms to constrained optimization with an additional cost critic-network and an adaptive Lagrangian multiplier. Moreover, considering the distribution nature of PCNs, in which each user schedules transactions independently, we develop a distributed training framework to collect the knowledge learned by each agent so as to enhance learning effectiveness. With the customized network design and the distributed training framework, PTRD achieves a good balance between the optimization of the throughput and the minimization of privacy risks. Evaluations show that PTRD outperforms the state-of-the-art PCN routing algorithms by 2.7%-62.5% in terms of the long-term throughput while satisfying privacy constraints.