{"main_file_link":[{"open_access":"1","url":"https://arxiv.org/abs/2104.04293"}],"scopus_import":"1","title":"LightPIR: Privacy-preserving route discovery for payment channel networks","project":[{"grant_number":"682815","_id":"258AA5B2-B435-11E9-9278-68D0E5697425","call_identifier":"H2020","name":"Teaching Old Crypto New Tricks"}],"citation":{"apa":"Pietrzak, K. Z., Salem, I., Schmid, S., & Yeo, M. X. (2021). LightPIR: Privacy-preserving route discovery for payment channel networks. Presented at the 2021 IFIP Networking Conference (IFIP Networking), Espoo and Helsinki, Finland: IEEE. https://doi.org/10.23919/IFIPNetworking52078.2021.9472205","chicago":"Pietrzak, Krzysztof Z, Iosif Salem, Stefan Schmid, and Michelle X Yeo. “LightPIR: Privacy-Preserving Route Discovery for Payment Channel Networks.” IEEE, 2021. https://doi.org/10.23919/IFIPNetworking52078.2021.9472205.","ama":"Pietrzak KZ, Salem I, Schmid S, Yeo MX. LightPIR: Privacy-preserving route discovery for payment channel networks. In: IEEE; 2021. doi:10.23919/IFIPNetworking52078.2021.9472205","mla":"Pietrzak, Krzysztof Z., et al. LightPIR: Privacy-Preserving Route Discovery for Payment Channel Networks. IEEE, 2021, doi:10.23919/IFIPNetworking52078.2021.9472205.","ieee":"K. Z. Pietrzak, I. Salem, S. Schmid, and M. X. Yeo, “LightPIR: Privacy-preserving route discovery for payment channel networks,” presented at the 2021 IFIP Networking Conference (IFIP Networking), Espoo and Helsinki, Finland, 2021.","short":"K.Z. Pietrzak, I. Salem, S. Schmid, M.X. Yeo, in:, IEEE, 2021.","ista":"Pietrzak KZ, Salem I, Schmid S, Yeo MX. 2021. LightPIR: Privacy-preserving route discovery for payment channel networks. 2021 IFIP Networking Conference (IFIP Networking)."},"oa_version":"Submitted Version","related_material":{"record":[{"status":"public","id":"14506","relation":"dissertation_contains"}]},"publication_identifier":{"eisbn":["978-3-9031-7639-3"],"eissn":["1861-2288"],"isbn":["978-1-6654-4501-6"]},"publication_status":"published","doi":"10.23919/IFIPNetworking52078.2021.9472205","department":[{"_id":"KrPi"}],"quality_controlled":"1","external_id":{"arxiv":["2104.04293"],"isi":["000853016800008"]},"date_updated":"2023-11-30T10:54:50Z","_id":"9969","ec_funded":1,"publisher":"IEEE","year":"2021","month":"06","isi":1,"language":[{"iso":"eng"}],"oa":1,"date_created":"2021-08-29T22:01:16Z","conference":{"name":"2021 IFIP Networking Conference (IFIP Networking)","end_date":"2021-06-24","start_date":"2021-06-21","location":"Espoo and Helsinki, Finland"},"type":"conference","article_processing_charge":"No","day":"21","user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8","abstract":[{"text":"Payment channel networks are a promising approach to improve the scalability of cryptocurrencies: they allow to perform transactions in a peer-to-peer fashion, along multihop routes in the network, without requiring consensus on the blockchain. However, during the discovery of cost-efficient routes for the transaction, critical information may be revealed about the transacting entities. This paper initiates the study of privacy-preserving route discovery mechanisms for payment channel networks. In particular, we present LightPIR, an approach which allows a client to learn the shortest (or cheapest in terms of fees) path between two nodes without revealing any information about the endpoints of the transaction to the servers. The two main observations which allow for an efficient solution in LightPIR are that: (1) surprisingly, hub labelling algorithms – which were developed to preprocess “street network like” graphs so one can later efficiently compute shortest paths – also perform well for the graphs underlying payment channel networks, and that (2) hub labelling algorithms can be conveniently combined with private information retrieval. LightPIR relies on a simple hub labeling heuristic on top of existing hub labeling algorithms which leverages the specific topological features of cryptocurrency networks to further minimize storage and bandwidth overheads. In a case study considering the Lightning network, we show that our approach is an order of magnitude more efficient compared to a privacy-preserving baseline based on using private information retrieval on a database that stores all pairs shortest paths.","lang":"eng"}],"author":[{"full_name":"Pietrzak, Krzysztof Z","id":"3E04A7AA-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0002-9139-1654","first_name":"Krzysztof Z","last_name":"Pietrzak"},{"last_name":"Salem","first_name":"Iosif","full_name":"Salem, Iosif"},{"last_name":"Schmid","full_name":"Schmid, Stefan","first_name":"Stefan"},{"last_name":"Yeo","first_name":"Michelle X","id":"2D82B818-F248-11E8-B48F-1D18A9856A87","full_name":"Yeo, Michelle X"}],"status":"public","date_published":"2021-06-21T00:00:00Z"}