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Privacy-Utility Tradeoffs in Routing Cryptocurrency over Payment Channel Networks

Published:12 June 2020Publication History
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Abstract

Payment channel networks (PCNs) are viewed as one of the most promising scalability solutions for cryptocurrencies today. Roughly, PCNs are networks where each node represents a user and each directed, weighted edge represents funds escrowed on a blockchain; these funds can be transacted only between the endpoints of the edge. Users efficiently transmit funds from node A to B by relaying them over a path connecting A to B, as long as each edge in the path contains enough balance (escrowed funds) to support the transaction. Whenever a transaction succeeds, the edge weights are updated accordingly. In deployed PCNs, channel balances (i.e., edge weights) are not revealed to users for privacy reasons; users know only the initial weights at time 0. Hence, when routing transactions, users typically first guess a path, then check if it supports the transaction. This guess-and-check process dramatically reduces the success rate of transactions. At the other extreme, knowing full channel balances can give substantial improvements in transaction success rate at the expense of privacy. In this work, we ask whether a network can reveal noisy channel balances to trade off privacy for utility. We show fundamental limits on such a tradeoff, and propose noise mechanisms that achieve the fundamental limit for a general class of graph topologies. Our results suggest that in practice, PCNs should operate either in the low-privacy or low-utility regime; it is not possible to get large gains in utility by giving up a little privacy, or large gains in privacy by sacrificing a little utility.

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          cover image Proceedings of the ACM on Measurement and Analysis of Computing Systems
          Proceedings of the ACM on Measurement and Analysis of Computing Systems  Volume 4, Issue 2
          SIGMETRICS
          June 2020
          623 pages
          EISSN:2476-1249
          DOI:10.1145/3405833
          Issue’s Table of Contents

          Copyright © 2020 ACM

          Publisher

          Association for Computing Machinery

          New York, NY, United States

          Publication History

          • Published: 12 June 2020
          • Online AM: 7 May 2020
          Published in pomacs Volume 4, Issue 2

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