Search arXivSearch

arXiv · 2604.06100

Signature Placement in Post-Quantum TLS Certificate Hierarchies: An Experimental Study of ML-DSA and SLH-DSA in TLS 1.3 Authentication

Abstract

Post-quantum migration in TLS 1.3 couples signature-algorithm choice with certificate-hierarchy structure, chain exposure during the handshake, and role-dependent cryptographic cost. In certificate-based authentication, the practical effect of a signature family depends on where it appears in the certification hierarchy, how much of that hierarchy is exposed during the handshake, and how the resulting cryptographic cost is distributed across client and server roles. Post-quantum TLS migration must therefore be evaluated as cryptographic design within authenticated key establishment, with algorithm selection assessed in its deployment context. This paper presents a local experimental study of TLS 1.3 authentication strategies implemented with OpenSSL 3 and oqsprovider. Using a reproducible laboratory setting, it compares ML-DSA and SLH-DSA across multiple certificate placements, hierarchy depths, and key-exchange modes, including classical, hybrid, and pure post-quantum configurations. The analysis is organized into four complementary campaigns: a leaf-only comparison, a full hierarchy strategy matrix, a depth comparison, and a key-exchange exploration. Across the experimental matrix, the main discontinuity appears when SLH-DSA is placed in the server leaf certificate. In that configuration, handshake latency and server-side compute cost increase by orders of magnitude, whereas strategies that confine SLH-DSA to upper trust layers and preserve ML-DSA in the interactive leaf remain within a more plausible operational range. The results also show that transport size alone does not explain the heavy regime: outside leaf-SLH scenarios, transferred bytes and observed chain size track latency closely, but once SLH-DSA reaches the leaf, server-side cryptographic cost becomes dominant.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

José Luis Delgado Jiménez. 2026-05-19. Signature Placement in Post-Quantum TLS Certificate Hierarchies: An Experimental Study of ML-DSA and SLH-DSA in TLS 1.3 Authentication. https://arxiv.org/abs/2604.06100

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Starfish: Rebalancing Multi-Party Off-Chain Payment Channels

Blockchain technology has revolutionized the way transactions are executed, but scalability remains a major challenge. Payment Channel Network (PCN), as a Layer-2 scaling solution, has been proposed to address this issue. However, skewed payments can deplete the balance of one party within a channel, restricting the ability of PCNs to transact through a path and subsequently reducing the transaction success rate. To address this issue, the technology of rebalancing has been proposed. However, existing rebalancing strategies in PCNs are limited in their efficiency and applicability under realistic network topologies. Cycle-based approaches only enable rebalancing among nodes that form cycles, while non-cycle-based approaches incur high on-chain operation costs. In this study, we propose Starfish, a rebalancing approach designed for the locally star-like structures formed by high-degree routing hubs and their incident channels in real-world PCNs. By leveraging a hub-centric structure, Starfish reduces the on-chain overhead of rebalancing from quadratic to linear in the number of channels, achieving high rebalancing efficiency. We formally prove the security of Starfish and conduct comparative experiments against existing rebalancing techniques to demonstrate its effectiveness.

cs.CR

Proof-of-Authorship for Diffusion-based AI Generated Content

Recent advancements in AI-generated content (AIGC) have introduced new challenges in intellectual property protection and the authentication of generated objects. We focus on scenarios in which an author seeks to assert authorship of an object generated using latent diffusion models (LDMs), in the presence of adversaries who attempt to falsely claim authorship of objects they did not create. While proof-of-ownership has been studied in the context of multimedia content through techniques such as time-stamping and watermarking, these approaches face notable limitations. In contrast to traditional content creation sources (e.g., cameras), the LDM generation process offers greater control to the author. Specifically, the random seed used during generation can be deliberately chosen. By binding the seed to the author's identity using cryptographic functions, the author can assert to be the creator of the object. We refer to this stronger guarantee as proof-of-authorship, since only the creator of the object can legitimately claim the object. This contrasts with proof-of-ownership via time-stamping or watermarking, where any entity could potentially claim ownership of an object by being the first to timestamp or embed the watermark. We propose a proof-of-authorship framework involving a probabilistic adjudicator who quantifies the probability that a claim is false. Furthermore, unlike prior approaches, the proposed framework does not involve any secret. We explore various attack scenarios and analyze design choices using Stable Diffusion 2.1 and XL as representative case studies.

cs.CR

Impact Analysis of Speech Representation Learning Models for Acoustic Side-Channel Attack

Acoustic side-channel attacks (ASCA) on keyboards have gained increasing attention, yet impact of speech representation learning models in ASCA remains unexplored. Addressing this, we introduce KEYAC, a dataset designed to analyze representation generalization for ASCA under both standard and VoIP codec settings. On KEYAC, we evaluate six representation learning models under zero-shot and partial fine-tuning settings using fully connected and convolutional networks. Results show that while partial fine-tuning improves performance, models struggle to generalize across VoIP codecs. We hypothesize this limitation stems from inadequate modeling of nonlinear feature interactions in conventional fine-tuning architectures. To address this, we employ Kolmogorov-Arnold Networks (KAN) for fine-tuning. Empirical results show that KAN-based fine-tuning consistently outperforms the baselines and establishes a new state-of-the-art on KEYAC.

cs.CR