A Comparative Analysis of Lightweight Hash Functions Using AVR ATXMega128 and ChipWhisperer
- URL: http://arxiv.org/abs/2508.07840v1
- Date: Mon, 11 Aug 2025 10:48:56 GMT
- Title: A Comparative Analysis of Lightweight Hash Functions Using AVR ATXMega128 and ChipWhisperer
- Authors: Mohsin Khan, Dag Johansen, HÃ¥vard Dagenborg,
- Abstract summary: This paper presents a comparative analysis of 22 key software-based lightweight hash functions.<n>We use a novel benchmark methodology that combines an microcontroller with the ChipWhisperer cryptanalysis platform.<n>We evaluate and compare the various hash functions along several dimensions, including execution speed, % measured in Cycles per Byte (CpB), memory footprint, and energy consumption.
- Score: 2.0765119902066864
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Lightweight hash functions have become important building blocks for security in embedded and IoT systems. A plethora of algorithms have been proposed and standardized, providing a wide range of performance trade-off options for developers to choose from. This paper presents a comparative analysis of 22 key software-based lightweight hash functions, including the finalist from the SHA-3 competition. We use a novel benchmark methodology that combines an AVR ATXMega128 microcontroller with the ChipWhisperer cryptanalysis platform and evaluate and compare the various hash functions along several dimensions, including execution speed, % measured in Cycles per Byte (CpB), memory footprint, and energy consumption. Using the composite E-RANK metric, we provide new insight into the various trade-offs each hash function offers to system developers.
Related papers
- Spotlight Attention: Towards Efficient LLM Generation via Non-linear Hashing-based KV Cache Retrieval [67.21678698740267]
We introduce Spotlight Attention, a novel method that employs non-linear hashing functions to optimize the embedding distribution of queries and keys.<n>We also develop a lightweight, stable training framework using a Bradley-Terry ranking-based loss.
arXiv Detail & Related papers (2025-08-27T10:11:27Z) - KEENHash: Hashing Programs into Function-Aware Embeddings for Large-Scale Binary Code Similarity Analysis [11.548924493185506]
KEENHash is a hashing approach that condenses a binary into one compact and fixed-length program embedding.<n>We show that KEENHash is at least 215 times faster than the state-of-the-art function matching tools.<n>In a large-scale scenario with 5.3 billion similarity evaluations, KEENHash takes only 395.83 seconds while these tools will cost at least 56 days.
arXiv Detail & Related papers (2025-06-13T09:33:58Z) - AutoSSVH: Exploring Automated Frame Sampling for Efficient Self-Supervised Video Hashing [72.10024026634976]
Self-Supervised Video Hashing (SSVH) compresses videos into hash codes for efficient indexing and retrieval using unlabeled training videos.<n>Existing approaches rely on random frame sampling to learn video features and treat all frames equally.<n>We propose a new framework, termed AutoSSVH, that employs adversarial frame sampling with hash-based contrastive learning.
arXiv Detail & Related papers (2025-04-04T16:56:17Z) - Gotta Hash 'Em All! Speeding Up Hash Functions for Zero-Knowledge Proof Applications [9.853088551679969]
HashEmAll is a novel collection of FPGA-based realizations for ZK-friendly hash functions.<n>Our evaluation shows that latency-optimized HashEmAll designs outperform CPU implementations by at least $10 times$.<n>This highlights the suitability of HashEmAll for real-world ZKP applications involving large-scale data authentication.
arXiv Detail & Related papers (2025-01-30T22:09:05Z) - A Method for Efficient Heterogeneous Parallel Compilation: A Cryptography Case Study [8.06660833012594]
This paper introduces a novel MLIR-based dialect, named hyper, designed to optimize data management and parallel computation across diverse hardware architectures.
We present HETOCompiler, a cryptography-focused compiler prototype that implements multiple hash algorithms and enables their execution on heterogeneous systems.
arXiv Detail & Related papers (2024-07-12T15:12:51Z) - Performance Evaluation of Hashing Algorithms on Commodity Hardware [0.0]
This report presents a performance evaluation of three popular hashing algorithms Blake3, SHA-256, and SHA-512.
These hashing algorithms are widely used in various applications, such as digital signatures, message authentication, and password storage.
The results of the evaluation show Blake3 generally outperforms both SHA-256 and SHA-512 in terms of throughput and latency.
arXiv Detail & Related papers (2024-07-11T08:31:02Z) - Efficient Hardware Implementation of Constant Time Sampling for HQC [2.5234156040689237]
HQC is one of the code-based finalists in the last round of the NIST post quantum cryptography standardization process.<n>A critical compute kernel with respect to efficient hardware implementations and security in HQC is the sampling method used to derive random numbers.<n>Due to its security criticality, recently an updated sampling algorithm was presented to increase its robustness against side-channel attacks.
arXiv Detail & Related papers (2023-09-28T14:57:48Z) - A Lower Bound of Hash Codes' Performance [122.88252443695492]
In this paper, we prove that inter-class distinctiveness and intra-class compactness among hash codes determine the lower bound of hash codes' performance.
We then propose a surrogate model to fully exploit the above objective by estimating the posterior of hash codes and controlling it, which results in a low-bias optimization.
By testing on a series of hash-models, we obtain performance improvements among all of them, with an up to $26.5%$ increase in mean Average Precision and an up to $20.5%$ increase in accuracy.
arXiv Detail & Related papers (2022-10-12T03:30:56Z) - Asymmetric Scalable Cross-modal Hashing [51.309905690367835]
Cross-modal hashing is a successful method to solve large-scale multimedia retrieval issue.
We propose a novel Asymmetric Scalable Cross-Modal Hashing (ASCMH) to address these issues.
Our ASCMH outperforms the state-of-the-art cross-modal hashing methods in terms of accuracy and efficiency.
arXiv Detail & Related papers (2022-07-26T04:38:47Z) - CARAFE++: Unified Content-Aware ReAssembly of FEatures [132.49582482421246]
We propose unified Content-Aware ReAssembly of FEatures (CARAFE++), a universal, lightweight and highly effective operator to fulfill this goal.
CARAFE++ generates adaptive kernels on-the-fly to enable instance-specific content-aware handling.
It shows consistent and substantial gains across all the tasks with negligible computational overhead.
arXiv Detail & Related papers (2020-12-07T07:34:57Z) - Pairwise Supervised Hashing with Bernoulli Variational Auto-Encoder and
Self-Control Gradient Estimator [62.26981903551382]
Variational auto-encoders (VAEs) with binary latent variables provide state-of-the-art performance in terms of precision for document retrieval.
We propose a pairwise loss function with discrete latent VAE to reward within-class similarity and between-class dissimilarity for supervised hashing.
This new semantic hashing framework achieves superior performance compared to the state-of-the-arts.
arXiv Detail & Related papers (2020-05-21T06:11:33Z) - Reinforcing Short-Length Hashing [61.75883795807109]
Existing methods have poor performance in retrieval using an extremely short-length hash code.
In this study, we propose a novel reinforcing short-length hashing (RSLH)
In this proposed RSLH, mutual reconstruction between the hash representation and semantic labels is performed to preserve the semantic information.
Experiments on three large-scale image benchmarks demonstrate the superior performance of RSLH under various short-length hashing scenarios.
arXiv Detail & Related papers (2020-04-24T02:23:52Z)
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.