Faster Post-Quantum TLS 1.3 Based on ML-KEM: Implementation and Assessment

• URL: http://arxiv.org/abs/2404.13544v2
• Date: Tue, 23 Apr 2024 03:53:09 GMT
• Title: Faster Post-Quantum TLS 1.3 Based on ML-KEM: Implementation and Assessment
• Abstract summary: TLS is extensively utilized for secure data transmission over networks. To counter quantum threats, it is imperative to integrate post-quantum algorithms into TLS. For the TLS protocol, handshake performance is crucial, and for post-quantum TLS (PQ-TLS) the performance of post-quantum key encapsulation mechanisms (KEMs) directly impacts handshake performance.
• Score: 5.483288991845528
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: TLS is extensively utilized for secure data transmission over networks. However, with the advent of quantum computers, the security of TLS based on traditional public-key cryptography is under threat. To counter quantum threats, it is imperative to integrate post-quantum algorithms into TLS. Most PQ-TLS research focuses on integration and evaluation, but few studies address the improvement of PQ-TLS performance by optimizing PQC implementation. For the TLS protocol, handshake performance is crucial, and for post-quantum TLS (PQ-TLS) the performance of post-quantum key encapsulation mechanisms (KEMs) directly impacts handshake performance. In this work, we explore the impact of post-quantum KEMs on PQ-TLS performance. We explore how to improve ML-KEM performance using the latest Intel's Advanced Vector Extensions instruction set AVX-512. We detail a spectrum of techniques devised to parallelize polynomial multiplication, modular reduction, and other computationally intensive modules within ML-KEM. Our optimized ML-KEM implementation achieves up to 1.64x speedup compared to the latest AVX2 implementation. Furthermore, we introduce a novel batch key generation method for ML-KEM that can seamlessly integrate into the TLS protocols. The batch method accelerates the key generation procedure by 3.5x to 4.9x. We integrate the optimized AVX-512 implementation of ML-KEM into TLS 1.3, and assess handshake performance under both PQ-only and hybrid modes. The assessment demonstrates that our faster ML-KEM implementation results in a higher number of TLS 1.3 handshakes per second under both modes. Additionally, we revisit two IND-1-CCA KEM constructions discussed in Eurocrypt22 and Asiacrypt23. Besides, we implement them based on ML-KEM and integrate the one of better performance into TLS 1.3 with benchmarks.

Related papers

• SpikeMLLM: Spike-based Multimodal Large Language Models via Modality-Specific Temporal Scales and Temporal Compression [46.709828328948724]
  Spiking Neural Networks (SNNs) offer inherent energy efficiency advantages on neuromorphic hardware.<n>We propose SpikeMLLM, the first spike-based framework for MLLMs.<n>We show that SpikeMLLM maintains near-lossless performance under aggressive timestep compression.
  arXiv  Detail & Related papers  (2026-04-13T15:32:44Z)
• Layered Performance Analysis of TLS 1.3 Handshakes: Classical, Hybrid, and Pure Post-Quantum Key Exchange [0.0]
  We study the impact of post-quantum cryptography (PQC) algorithms on multiple layers of stateful HTTP over TLS transactions.<n>We propose a laboratory architecture that emulates a real-world setup in which a load test of up to 100 transactions per second is sent to a load balancer.<n>Each set of tests is executed using the TLS 1.3 key exchange groups as follows: traditional (or non-PQC), hybrid PQC and pure PQC.
  arXiv  Detail & Related papers  (2026-03-11T17:27:41Z)
• Post-Quantum Entropy as a Service for Embedded Systems [0.0]
  We build a Quantum Entropy as a Service (QE) system that moves QRNG-derived entropy from a Quantis device to ESP32-class clients over post-quantum-secured channels.<n>On the server side, the design exposes two paths: direct quantum entropy through a custom OpenSSL provider and mixed entropy through the Linux system pool.
  arXiv  Detail & Related papers  (2026-03-10T23:09:55Z)
• A Lightweight Defense Mechanism against Next Generation of Phishing Emails using Distilled Attention-Augmented BiLSTM [34.0814379994364]
  The MobileBERT teacher receives fine-tuning before its transformation into a BiLSTM model with multi-head attention.<n>The system demonstrates excellent performance in terms of accuracy and latency while maintaining a compact size.<n>The paper examines system performance under high traffic conditions and security measures for privacy protection and implementation methods for operational deployment.
  arXiv  Detail & Related papers  (2026-02-24T20:06:45Z)
• Towards Quantum-Safe O-RAN -- Experimental Evaluation of ML-KEM-Based IPsec on the E2 Interface [35.51102338121691]
  We experimentally evaluate the impact of integrating a NIST-aligned module-lattice KEM into IKEv2/IPsec protecting the E2 interface between the 5G Node B and the Near-Real-Time RAN Intelligent Controller.<n>Results show that ML-KEM integration adds a small overhead to tunnel establishment, which is approximately 35 ms in comparison to classical IPsec.<n>These findings indicate that ML-KEM based IPsec on the E2 interface is practically feasible and inform quantum-safe migration strategies for O-RAN deployments.
  arXiv  Detail & Related papers  (2026-01-28T08:44:16Z)
• Small LLMs with Expert Blocks Are Good Enough for Hyperparamter Tuning [0.0]
  We propose an Expert Block Framework for HPT using Small Language Models.<n>At its core is the Trajectory Context Summarizer (TCS), a deterministic block that transforms raw training trajectories into structured context.<n>Our TCS-enabled HPT pipeline achieves average performance within 0.9 percentage points of GPT-4 across six diverse tasks.
  arXiv  Detail & Related papers  (2025-09-19T03:46:42Z)
• Analysis of Post-Quantum Cryptography in User Equipment in 5G and Beyond [5.422409088453788]
  Quantum computing has led to the transition to post-quantum cryptography (PQC)<n>This paper presents a detailed implementation and performance evaluation of NIST-selected PQC algorithms in user equipment (UE) to UE communications over 5G networks.<n>We evaluate performance based on handshake latency, CPU and memory usage, bandwidth, and retransmission rates, under varying cryptographic configurations and client loads.
  arXiv  Detail & Related papers  (2025-07-22T23:21:16Z)
• How to Train Your LLM Web Agent: A Statistical Diagnosis [102.04125085041473]
  We present the first statistically grounded study on compute allocation for LLM web-agent post-training.<n>Our approach uses a two-stage pipeline, training a Llama 3.1 8B student to imitate a Llama 3.3 70B teacher via supervised fine-tuning (SFT) and on-policy reinforcement learning.<n>Our results show that combining SFT with on-policy RL consistently outperforms either approach alone on both WorkArena and MiniWob++.
  arXiv  Detail & Related papers  (2025-07-05T17:12:33Z)
• Accelerating Parameter Initialization in Quantum Chemical Simulations via LSTM-FC-VQE [5.396660696277483]
  We use Long Short-Term Memory neural networks to speed up quantum chemical simulations.<n>By training the LSTM on optimized parameters from small molecules, the model learns to predict high-quality initializations for larger systems.
  arXiv  Detail & Related papers  (2025-05-16T04:19:00Z)
• Assessing Quantum Layout Synthesis Tools via Known Optimal-SWAP Cost Benchmarks [5.462554121956812]
  We introduce QUBIKOS, a benchmark set with provable-optimal SWAP counts and non-trivial circuit structures. For the first time, we are able to quantify the optimality gaps of SWAP gate usages of the leading QLS algorithms.
  arXiv  Detail & Related papers  (2025-02-12T23:13:01Z)
• Can 1B LLM Surpass 405B LLM? Rethinking Compute-Optimal Test-Time Scaling [69.57918638435491]
  Test-Time Scaling is an important method for improving the performance of Large Language Models. This paper focuses on two core questions: What is the optimal approach to scale test-time computation across different policy models, PRMs, and problem difficulty levels? We show that with our compute-optimal TTS strategy, extremely small policy models can outperform larger models.
  arXiv  Detail & Related papers  (2025-02-10T17:30:23Z)
• Kimi k1.5: Scaling Reinforcement Learning with LLMs [84.2229964736678]
  We report on the training practice of Kimi k1.5, our latest multi-modal language model trained with reinforcement learning. Long context scaling and improved policy optimization methods are key ingredients of our approach. Our system achieves state-of-the-art reasoning performance across multiple benchmarks and modalities.
  arXiv  Detail & Related papers  (2025-01-22T02:48:14Z)
• Evaluating Post-Quantum Cryptography on Embedded Systems: A Performance Analysis [7.142158555793151]
  NIST has finalized the selection of post-quantum cryptographic (PQC) algorithms for use in the era of quantum computing. There is limited study on profiling these newly standardized algorithms in resource-constrained communication systems.
  arXiv  Detail & Related papers  (2024-09-09T03:12:28Z)
• Block encoding by signal processing [0.0]
  We demonstrate that QSP-based techniques, such as Quantum Singular Value Transformation (QSVT) and Quantum Eigenvalue Transformation for Unitary Matrices (QETU) can themselves be efficiently utilized for BE implementation. We present several examples of using QSVT and QETU algorithms, along with their combinations, to block encode Hamiltonians for lattice bosons. We find that, while using QSVT for BE results in the best gate count scaling with the number of qubits per site, LOVE-LCU outperforms all other methods for operators acting on up to $lesssim11$ qubits.
  arXiv  Detail & Related papers  (2024-08-29T18:00:02Z)
• Quantum Long Short-Term Memory for Drug Discovery [15.186004892998382]
  We present Quantum Long Short-Term Memory (QLSTM), a QML architecture, and demonstrate its effectiveness in drug discovery.<n>We observe consistent performance gains over classical LSTM, with ROC-AUC improvements ranging from 3% to over 6%.<n>QLSTM exhibits improved predictive accuracy as the number of qubits increases, and faster convergence than classical LSTM.
  arXiv  Detail & Related papers  (2024-07-29T10:10:03Z)
• SliM-LLM: Salience-Driven Mixed-Precision Quantization for Large Language Models [67.67135738642547]
  Post-training quantization (PTQ) is a powerful compression technique investigated in large language models (LLMs) Existing PTQ methods are not ideal in terms of accuracy and efficiency, especially with below 4 bit-widths. This paper presents a Salience-Driven Mixed-Precision Quantization scheme for LLMs, namely SliM-LLM.
  arXiv  Detail & Related papers  (2024-05-23T16:21:48Z)
• AffineQuant: Affine Transformation Quantization for Large Language Models [58.45460102764]
  Post-Training Quantization (PTQ) has emerged as a subject of considerable interest due to its compression efficiency and cost-effectiveness in the context of training. Existing PTQ methods for Large-scale Language Models (LLMs) limit the optimization scope to scaling transformations between pre- and post-quantization weights. In this paper, we advocate for the direct optimization using equivalent Affine transformations in PTQ (AffineQuant)
  arXiv  Detail & Related papers  (2024-03-19T08:40:21Z)
• Eyes Closed, Safety On: Protecting Multimodal LLMs via Image-to-Text Transformation [98.02846901473697]
  We propose ECSO (Eyes Closed, Safety On), a training-free protecting approach that exploits the inherent safety awareness of MLLMs. ECSO generates safer responses via adaptively transforming unsafe images into texts to activate the intrinsic safety mechanism of pre-aligned LLMs.
  arXiv  Detail & Related papers  (2024-03-14T17:03:04Z)
• BiLLM: Pushing the Limit of Post-Training Quantization for LLMs [53.31402059062365]
  BiLLM is a groundbreaking 1-bit post-training quantization scheme tailored for pretrained large language models. It achieves for the first time high-accuracy inference (e.g. 8.41 perplexity on LLaMA2-70B) with only 1.08-bit weights across various LLMs families.
  arXiv  Detail & Related papers  (2024-02-06T09:26:34Z)
• Extreme Compression of Large Language Models via Additive Quantization [59.3122859349777]
  Our algorithm, called AQLM, generalizes the classic Additive Quantization (AQ) approach for information retrieval. We provide fast GPU and CPU implementations of AQLM for token generation, which enable us to match or outperform optimized FP16 implementations for speed.
  arXiv  Detail & Related papers  (2024-01-11T18:54:44Z)
• FIPS Compliant Quantum Secure Communication using Quantum Permutation Pad [0.602276990341246]
  This paper proposes to take a nested mode to enable TLS 1.3 protocol with quantum-safe data, which can be made available today and is compliant. We discussed the performance impacts of the handshaking phase of the nested TLS 1.3 with PQC and the symmetric encryption phase.
  arXiv  Detail & Related papers  (2022-12-30T21:56:35Z)
• A Multi-Stage Multi-Codebook VQ-VAE Approach to High-Performance Neural TTS [52.51848317549301]
  We propose a Multi-Stage, Multi-Codebook (MSMC) approach to high-performance neural TTS synthesis. A vector-quantized, variational autoencoder (VQ-VAE) based feature analyzer is used to encode Mel spectrograms of speech training data. In synthesis, the neural vocoder converts the predicted MSMCRs into final speech waveforms.
  arXiv  Detail & Related papers  (2022-09-22T09:43:17Z)
• Efficient experimental characterization of quantum processes via compressed sensing on an NMR quantum processor [4.291616110077346]
  We employ the compressed sensing (CS) algorithm and a heavily reduced data set to experimentally perform true quantum process tomography (QPT) on an NMR quantum processor. We obtain the estimate of the process matrix $chi$ corresponding to various two- and three-qubit quantum gates with a high fidelity. We also experimentally characterized the reduced dynamics of a two-qubit subsystem embedded in a three-qubit system.
  arXiv  Detail & Related papers  (2021-09-27T17:05:13Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.