On the Impact of Attachment Strategies for Payment Channel Networks

Payment channel networks, such as Bitcoin’s Lightning Network, promise to improve the scalability of blockchain systems by processing the majority of transactions off-chain. Due to the design, the positioning of nodes in the network topology is a highly influential factor regarding the experienced performance, costs, and fee revenue of network participants. As a consequence, today’s Lightning Network is built around a small number of highly-connected hubs. Recent literature shows the centralizing tendencies to be incentive-compatible and at the same time detrimental to security and privacy. The choice of attachment strategies therefore becomes a crucial factor for the future of such systems. In this paper, we provide an empirical study on the (local and global) impact of various attachment strategies for payment channel networks. To this end, we introduce candidate strategies from the field of graph theory and analyze them with respect to their computational complexity as well as their repercussions for end users and service providers. Moreover, we evaluate their long-term impact on the network topology.

[1]  George Danezis,et al.  Sphinx: A Compact and Provably Secure Mix Format , 2009, 2009 30th IEEE Symposium on Security and Privacy.

[2]  Ulrik Brandes,et al.  On variants of shortest-path betweenness centrality and their generic computation , 2008, Soc. Networks.

[3]  Andrew Miller,et al.  An Empirical Analysis of Privacy in the Lightning Network , 2021, Financial Cryptography.

[4]  Edsger W. Dijkstra,et al.  A note on two problems in connexion with graphs , 1959, Numerische Mathematik.

[5]  Rami Khalil,et al.  Revive: Rebalancing Off-Blockchain Payment Networks , 2017, IACR Cryptol. ePrint Arch..

[6]  Teofilo F. GONZALEZ,et al.  Clustering to Minimize the Maximum Intercluster Distance , 1985, Theor. Comput. Sci..

[7]  Aviv Zohar,et al.  Flood & Loot: A Systemic Attack on The Lightning Network , 2020, AFT.

[8]  Ferenc Beres,et al.  A Cryptoeconomic Traffic Analysis of Bitcoins Lightning Network , 2019, ArXiv.

[9]  Giulio Malavolta,et al.  Concurrency and Privacy with Payment-Channel Networks , 2017, IACR Cryptol. ePrint Arch..

[10]  Adam Meyerson,et al.  Minimizing Average Shortest Path Distances via Shortcut Edge Addition , 2009, APPROX-RANDOM.

[11]  David Tse,et al.  Boomerang: Redundancy Improves Latency and Throughput in Payment-Channel Networks , 2020, Financial Cryptography.

[12]  Miriam Baglioni,et al.  Fast Exact Computation of betweenness Centrality in Social Networks , 2012, 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining.

[13]  Toshiaki Miyazaki,et al.  Secure Balance Planning of Off-blockchain Payment Channel Networks , 2020, IEEE INFOCOM 2020 - IEEE Conference on Computer Communications.

[14]  Andrew Miller,et al.  Sprites: Payment Channels that Go Faster than Lightning , 2017, ArXiv.

[15]  Aviv Zohar,et al.  Congestion Attacks in Payment Channel Networks , 2020, Financial Cryptography.

[16]  Marek Chrobak,et al.  The reverse greedy algorithm for the metric k-median problem , 2005, Inf. Process. Lett..

[17]  Feng Hao,et al.  Towards Bitcoin Payment Networks , 2016, ACISP.

[18]  David B. Shmoys,et al.  A Best Possible Heuristic for the k-Center Problem , 1985, Math. Oper. Res..

[19]  Ralph Holz,et al.  An empirical study of availability and reliability properties of the Bitcoin Lightning Network , 2020, ArXiv.

[20]  Giulio Malavolta,et al.  Anonymous Multi-Hop Locks for Blockchain Scalability and Interoperability , 2019, NDSS.

[21]  Florian Tschorsch,et al.  Counting Down Thunder: Timing Attacks on Privacy in Payment Channel Networks , 2020, AFT.

[22]  Pedro Moreno-Sanchez,et al.  A Quantitative Analysis of Security, Anonymity and Scalability for the Lightning Network , 2020, 2020 IEEE European Symposium on Security and Privacy Workshops (EuroS&PW).

[23]  Pierluigi Crescenzi,et al.  Improving the Betweenness Centrality of a Node by Adding Links , 2017, ACM J. Exp. Algorithmics.

[24]  Florian Tschorsch,et al.  Discharged Payment Channels: Quantifying the Lightning Network's Resilience to Topology-Based Attacks , 2019, 2019 IEEE European Symposium on Security and Privacy Workshops (EuroS&PW).

[25]  Zekeriya Erkin,et al.  How to profit from payments channels , 2019, Financial Cryptography.

[26]  Aviv Zohar,et al.  Optimizing Off-Chain Payment Networks in Cryptocurrencies , 2020, ArXiv.

[27]  Christian Decker,et al.  Lightning network: a second path towards centralisation of the Bitcoin economy , 2020, New Journal of Physics.

[28]  Stefan Schmid,et al.  Hijacking Routes in Payment Channel Networks: A Predictability Tradeoff , 2019, ArXiv.

[29]  Rene Pickhardt,et al.  Imbalance measure and proactive channel rebalancing algorithm for the Lightning Network , 2019, 2020 IEEE International Conference on Blockchain and Cryptocurrency (ICBC).

[30]  Chen Feng,et al.  A Measurement Study of Bitcoin Lightning Network , 2019, 2019 IEEE International Conference on Blockchain (Blockchain).

[31]  Albert,et al.  Emergence of scaling in random networks , 1999, Science.

[32]  Pramod Viswanath,et al.  Routing Cryptocurrency with the Spider Network , 2018, HotNets.

[33]  Leonard M. Freeman,et al.  A set of measures of centrality based upon betweenness , 1977 .

[34]  Roger Wattenhofer,et al.  Ride the Lightning: The Game Theory of Payment Channels , 2019, Financial Cryptography.

[35]  László Gulyás,et al.  Topological Analysis of Bitcoin's Lightning Network , 2019, MARBLE.

[36]  Christian Decker,et al.  A Fast and Scalable Payment Network with Bitcoin Duplex Micropayment Channels , 2015, SSS.