Panos Kampanakis and Will Childs-Klein (AWS)

It has been shown that post-quantum key exchange and authentication with ML-KEM and ML-DSA, NIST’s post-quantum algorithm picks, will have an impact on TLS 1.3 performance used in the Web or other applications. Studies so far have focused on the overhead of quantum-resistant algorithms on TLS time-to-first-byte (handshake time). Although these works have been important in quantifying the slowdown in connection establishment, they do not capture the full picture regarding real-world TLS 1.3 connections which carry sizable amounts of data. Intuitively, the introduction of an extra 10KB of ML-KEM and ML-DSA exchanges in the connection negotiation will inflate the connection establishment time proportionally more than it will increase the total connection time of a Web connection carrying 200KB of data. In this work, we quantify the impact of ML-KEM and ML-DSA on typical TLS 1.3 connections which transfer a few hundreds of KB from the server to the client. We study the slowdown in the time-to-last-byte of post-quantum connections under normal network conditions and in more unstable environments with high packet delay variability and loss probabilities. We show that the impact of ML-KEM and ML-DSA on the TLS 1.3 time-to-last-byte under stable network conditions is lower than the impact on the time-to-first-byte and diminishes as the transferred data increases. The time-to-last-byte increase stays below 5% for high-bandwidth, stable networks. It goes from 32% increase of the time-to-first-byte to under 15% increase of the time-to-last-byte when transferring 50KiB of data or more under low-bandwidth, stable network conditions. Even when congestion control affects connection establishment, the additional slowdown drops below 10% as the connection data increases to 200KiB. We also show that connections in lossy or volatile networks could see higher impact from post-quantum handshakes, but these connections’ time-to-last-byte degradation still drops as the transferred data increases. Finally, we show that such connections are already significantly slow and volatile regardless of the TLS handshake.

View More Papers

What the Fork? Finding and Analyzing Malware in GitHub...

Alan Cao (New York University) and Brendan Dolan-Gavitt (New York University)

Read More

Securing the Satellite Software Stack

Samuel Jero (MIT Lincoln Laboratory), Juliana Furgala (MIT Lincoln Laboratory), Max A Heller (MIT Lincoln Laboratory), Benjamin Nahill (MIT Lincoln Laboratory), Samuel Mergendahl (MIT Lincoln Laboratory), Richard Skowyra (MIT Lincoln Laboratory)

Read More

Towards Precise Reporting of Cryptographic Misuses

Yikang Chen (The Chinese University of Hong Kong), Yibo Liu (Arizona State University), Ka Lok Wu (The Chinese University of Hong Kong), Duc V Le (Visa Research), Sze Yiu Chau (The Chinese University of Hong Kong)

Read More

Heterogeneous Graph Pre-training Based Model for Secure and Efficient...

Xurui Li (Fudan University), Xin Shan (Bank of Shanghai), Wenhao Yin (Shanghai Saic Finance Co., Ltd)

Read More