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

ditto: WAN Traffic Obfuscation at Line Rate

Roland Meier (ETH Zürich), Vincent Lenders (armasuisse), Laurent Vanbever (ETH Zürich)

Read More

Above and Beyond: Organizational Efforts to Complement U.S. Digital...

Rock Stevens (University of Maryland), Faris Bugra Kokulu (Arizona State University), Adam Doupé (Arizona State University), Michelle L. Mazurek (University of Maryland)

Read More

Detecting Obfuscated Function Clones in Binaries using Machine Learning

Michael Pucher (University of Vienna), Christian Kudera (SBA Research), Georg Merzdovnik (SBA Research)

Read More

DRIVETRUTH: Automated Autonomous Driving Dataset Generation for Security Applications

Raymond Muller (Purdue University), Yanmao Man (University of Arizona), Z. Berkay Celik (Purdue University), Ming Li (University of Arizona) and Ryan Gerdes (Virginia Tech)

Read More