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Faster Post-Quantum TLS Handshakes Without Intermediate CA Certificates

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Cyber Security, Cryptology, and Machine Learning (CSCML 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13301))

Abstract

Traditionally, the most data-heavy part of a (D)TLS handshake has been authentication which includes a handshake signature and digital certificates. Although most common (D)TLS usecases are not significantly affected, some constrained ones such as low bandwidth environments or delay sensitive applications can see drastic performance degradation due to big certificates or certificate chains. That has led the security community to seek options to alleviate the issue. Post-quantum signatures and keys, on the other hand, have been proven to noticeably slow down handshakes even for common Internet (D)TLS or QUIC applications due to the significantly higher amounts of post-quantum authentication data they include. In this work, we quantify the size issue of post-quantum certificates in (D)TLS and QUIC and make the case for speeding up (D)TLS and QUIC handshakes by omitting the intermediate certificate authority certificates in the handshake. We present how that can be achieved along with the usecases that will mostly benefit from such a mechanism. We offer quantitative analyses to show that this approach is relatively straightforward, backwards compatible and with little overhead introduced for caching the certificates. We also discuss caching mechanisms based on different optimization goals.

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Notes

  1. 1.

    Because certificate validation requires that root keys be distributed independently, the self-signed certificate that specifies the root certificate authority MAY be omitted from the chain, under the assumption that the remote end must already possess it in order to validate it in any case [7, 30].

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Kampanakis, P., Kallitsis, M. (2022). Faster Post-Quantum TLS Handshakes Without Intermediate CA Certificates. In: Dolev, S., Katz, J., Meisels, A. (eds) Cyber Security, Cryptology, and Machine Learning. CSCML 2022. Lecture Notes in Computer Science, vol 13301. Springer, Cham. https://doi.org/10.1007/978-3-031-07689-3_25

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  • DOI: https://doi.org/10.1007/978-3-031-07689-3_25

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