Paper 2025/2235

MLWE’s impact on Web Metrics, mTLS TTLB, and AWS service endpoint connections

Abstract

Migrating to quantum-resistant cryptographic algorithms, specifically the NIST-standardized Module Learning with Errors (MLWE) primitives, would inevitably result in data transmission overhead in secure transport protocols due to their larger key, ciphertext, and signature sizes. Would the connection setup cost noticeably affect application performance? This study evaluates MLWE's performance impact on practical use cases that rely on TLS 1.3 via real-world experiments. We analyze three distinct scenarios by sharing empirical and experimental data of applications interfacing with cloud service TLS endpoints, Web user metrics, and mutual TLS connections. We argue that some cloud applications will not be significantly affected due to their unconstrained environment. We show that Web performance degradation will remain below 10% for common webpages, corresponding to time delays of under 100ms, which users are unlikely to perceive. For mutual TLS applications, our experiments show that MLWE noticeably affects Time-to-First-Byte, almost doubling the connection times compared to plain TLS. However, when evaluating Time-to-Last-Byte, a metric more closely tied to application performance, the overall impact drops to about 15% for ~150KB data transfers in fast or slow networks. This impact is much lower for large client-server round trips. While these results are reassuring that MLWE could unnoticeably be introduced in common TLS use cases, they do not diminish the value of data trimming techniques proposed in the literature (e.g., session resumption, intermediate certificate authority suppression) to speed up connections.