Astrape: Anonymous Payment Channels with Boring Cryptography

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Abstract

The increasing use of blockchain-based cryptocurrencies like Bitcoin has run into inherent scalability limitations of blockchains. Payment channel networks, or PCNs, promise to greatly increase scalability by conducting the vast majority of transactions outside the blockchain while leveraging it as a final settlement protocol. Unfortunately, first-generation PCNs have significant privacy flaws. In particular, even though transactions are conducted off-chain, anonymity guarantees are very weak. In this work, we present Astrape, a novel PCN construction that achieves strong security and anonymity guarantees with simple, black-box cryptography, given a blockchain with flexible scripting. Existing anonymous PCN constructions often integrate with specific, often custom-designed, cryptographic constructions. But at a slight cost to asymptotic performance, Astrape can use any generic public-key signature scheme and any secure hash function, modeled as a random oracle, to achieve strong anonymity, by using a unique construction reminiscent of onion routing. This allows Astrape to achieve provable security that is “generic” over the computational hardness assumptions of the underlying primitives. Astrape’s simple cryptography also lends itself to more straightforward security proofs compared to existing systems. Furthermore, we evaluate Astrape’s performance, including that of a concrete implementation on the Bitcoin Cash blockchain. We show that despite worse theoretical time complexity compared to state-of-the-art systems that use custom cryptography, Astrape operations on average have a very competitive performance of less than 10 ms of computation and 1 KB of communication on commodity hardware. Astrape explores a new avenue to secure and anonymous PCNs that achieves similar or better performance compared to existing solutions.

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APA

Dong, Y., Goldberg, I., Gorbunov, S., & Boutaba, R. (2022). Astrape: Anonymous Payment Channels with Boring Cryptography. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 13269 LNCS, pp. 748–768). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-09234-3_37

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