Zhonghui Ge (Shanghai Jiao Tong University), Yi Zhang (Shanghai Jiao Tong University), Yu Long (Shanghai Jiao Tong University), Dawu Gu (Shanghai Jiao Tong University)

A leading approach to enhancing the performance and scalability of permissionless blockchains is to use the payment channel, which allows two users to perform off-chain payments with almost unlimited frequency. By linking payment channels together to form a payment channel network, users connected by a path of channels can perform off-chain payments rapidly. However, payment channels risk encountering fund depletion, which threatens the availability of both the payment channel and network. The most recent method needs a cycle-based channel rebalancing procedure, which requires a fair leader and users with rebalancing demands forming directed cycles in the network. Therefore, its large-scale applications are restricted.

In this work, we introduce Shaduf, a novel non-cycle off-chain rebalancing protocol that offers a new solution for users to shift coins between channels directly without relying on the cycle setting. Shaduf can be applied to more general rebalancing scenarios. We provide the details of Shaduf and formally prove its security under the Universal Composability framework. Our prototype demonstrates its feasibility and the experimental evaluation shows that Shaduf enhances the Lighting Network performance in payment success ratio and volume. Moreover, our protocol prominently reduces users’ deposits in channels while maintaining the same amount of payments.

View More Papers

SpiralSpy: Exploring a Stealthy and Practical Covert Channel to...

Zhengxiong Li (University at Buffalo, SUNY), Baicheng Chen (University at Buffalo), Xingyu Chen (University at Buffalo), Huining Li (SUNY University at Buffalo), Chenhan Xu (University at Buffalo, SUNY), Feng Lin (Zhejiang University), Chris Xiaoxuan Lu (University of Edinburgh), Kui Ren (Zhejiang University), Wenyao Xu (SUNY Buffalo)

Read More

Trust and Privacy Expectations during Perilous Times of Contact...

Habiba Farzand (University of Glasgow), Florian Mathis (University of Glasgow), Karola Marky (University of Glasgow), Mohamed Khamis (University of Glasgow)

Read More

Context-Sensitive and Directional Concurrency Fuzzing for Data-Race Detection

Zu-Ming Jiang (Tsinghua University), Jia-Ju Bai (Tsinghua University), Kangjie Lu (University of Minnesota), Shi-Min Hu (Tsinghua University)

Read More

MIRROR: Model Inversion for Deep LearningNetwork with High Fidelity

Shengwei An (Purdue University), Guanhong Tao (Purdue University), Qiuling Xu (Purdue University), Yingqi Liu (Purdue University), Guangyu Shen (Purdue University); Yuan Yao (Nanjing University), Jingwei Xu (Nanjing University), Xiangyu Zhang (Purdue University)

Read More