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Payment channel networks are a promising solution to the scalability challenge of blockchains and are designed for significantly increased transaction throughput compared to the layer one blockchain. Since payment channel networks are essentially decentralized peerto- peer networks, routing transactions is a fundamental challenge. Payment channel networks have some unique security and privacy requirements that make pathfinding challenging, for instance, network topology is not publicly known, and sender/receiver privacy should be preserved, in addition to providing atomicity guarantees for payments. In this paper, we present an efficient privacypreserving routing protocol, SPRITE, for payment channel networks that supports concurrent transactions. By finding paths offline and processing transactions online, SPRITE can process transactions in just two rounds, which is more efficient compared to prior work. We evaluate SPRITE’s performance using Lightning Network data and prove its security using the Universal Composability framework. In contrast to the current cutting-edge methods that achieve rapid transactions, our approach significantly reduces the message complexity of the system by 3 orders of magnitude while maintaining similar latencies.
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DOCSDN: Dynamic and Optimal Configuration of Software-Defined Networks
Networks are designed with functionality, security, performance, and cost in mind. Tools exist to check or optimize individual properties of a network. These properties may conflict, so it is not always possible to run these tools in series to find a configuration that meets all requirements. This leads to network administrators manually searching for a configuration. This need not be the case. In this paper, we introduce a layered framework for optimizing network configuration for functional and security requirements. Our framework is able to output configurations that meet reachability, bandwidth, and risk requirements. Each layer of our framework optimizes over a single property. A lower layer can constrain the search problem of a higher layer allowing the framework to converge on a joint solution. Our approach has the most promise for software-defined networks which can easily reconfigure their logical configuration. Our approach is validated with experiments over the fat tree topology, which is commonly used in data center networks. Search terminates in between 1–5 min in experiments. Thus, our solution can propose new configurations for short term events such as defending against a focused network attack.
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- Award ID(s):
- 1849904
- PAR ID:
- 10209269
- Date Published:
- Journal Name:
- Information Security and Privacy. ACISP 2019
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1007/978-3-030-21548-4_25
