Xiaoyu Qiu, Wuhui Chen, Bingxin Tang, Junyuan Liang, Hong-Ning Dai, and Zibin Zheng
IEEE Transactions on Dependable and Secure Computing (early access)
Publication year: 2022


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.


@ARTICLE {9927465,
   author = {X. Qiu and W. Chen and B. Tang and J. Liang and H.-N. Dai and Z. Zheng},
   journal = {IEEE Transactions on Dependable and Secure Computing},
   title = {A Distributed and Privacy-Aware High-Throughput Transaction Scheduling Approach for Scaling Blockchain},
   year = {2022},
   volume = {},
   number = {01},
   pages = {1-15},
   doi = {10.1109/TDSC.2022.3216571},
   publisher = {IEEE Computer Society},
   month = {oct}