A lattice-based key-insulated and privacy-preserving signature scheme with publicly derived public key

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Abstract

As a widely used privacy-preserving technique for cryptocurrencies, Stealth Address constitutes a key component of Ring Confidential Transaction (RingCT) protocol and it was adopted by Monero, one of the most popular privacy-centric cryptocurrencies. Recently, Liu et al. [EuroS&P 2019] pointed out a flaw in the current widely used stealth address algorithm that once a derived secret key is compromised, the damage will spread to the corresponding master secret key, and all the derived secret keys thereof. To address this issue, Liu et al. introduced Key-Insulated and Privacy-Preserving Signature Scheme with Publicly Derived Public Key (PDPKS scheme), which captures the functionality, security, and privacy requirements of stealth address in cryptocurrencies. They further proposed a paring-based PDPKS construction and thus provided a provably secure stealth address algorithm. However, while other privacy-preserving cryptographic tools for RingCT, such as ring signature, commitment, and range proof, have successfully found counterparts on lattices, the development of lattice-based stealth address scheme lags behind and hinders the development of quantum-resistant privacy-centric cryptocurrencies following the RingCT approach. In this paper, we propose the first lattice-based PDPKS scheme and prove its security in the random oracle model. The scheme provides (potentially) quantum security not only for the stealth address algorithm but also for the deterministic wallet. Prior to this, the existing deterministic wallet algorithms, which have been widely adopted by most Bitcoin-like cryptocurrencies due to its easy backup/recovery and trustless audits, are not quantum resistant.

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APA

Liu, W., Liu, Z., Nguyen, K., Yang, G., & Yu, Y. (2020). A lattice-based key-insulated and privacy-preserving signature scheme with publicly derived public key. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 12309 LNCS, pp. 357–377). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-59013-0_18

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