LiteHash:适用于资源受限硬件的哈希函数

IF 2.8 3区 计算机科学 Q2 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE ACM Transactions on Embedded Computing Systems Pub Date : 2024-07-09 DOI:10.1145/3677181
Sagar Dev Achar, Thejaswini P, Sukumar Nandi, S. Nandi
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引用次数: 0

摘要

全球向边缘计算的模式转变导致对高效完整性验证的需求日益增长。哈希函数是一种单向算法,可作为数据内容的零知识证明。然而,在处理能力和内存有限的设备上计算哈希值是不可行的。因此,我们提出了四种新颖的 LiteHash 函数,它们在架构上与 SHA-512 相似,但更加简单。通过使用各种近似技术,我们的实现降低了将信息摘要为哈希值的计算成本。在使用 NIST PRNG 测试套件验证我们提出的设计时,我们观察到与 SHA-512 同等的加密安全性,同时满足所有所需的哈希函数属性要求。我们观察到面积至少减少了 9.41%,功耗降低了 20.47%,吞吐量提高了 22.05%。我们的设计可提供高达 2 Gbps 的吞吐量,同时面积和功耗分别减少了 16.86% 和 32.48%。LiteHash 函数还支持计算整个 SHA-2 系列的散列函数(SHA-224/256/384/512),只需对架构稍作修改即可。
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LiteHash: Hash Functions for Resource-Constrained Hardware
The global paradigm shift towards edge computing has led to a growing demand for efficient integrity verification. Hash functions are one-way algorithms which act as a zero-knowledge proof of a datum’s contents. However, it is infeasible to compute hashes on devices with limited processing power and memory. Hence, we propose four novel LiteHash functions which are architecturally similar to SHA-512 yet simpler. By using various approximation techniques, our implementations reduce the computational costs of digesting a message into a hash. On validating our proposed designs using the NIST PRNG Test Suite, we observe SHA-512 equivalent cryptographic security while satisfying all desired hash function property requirements. We observe a minimum of 9.41% reduction in area, 20.47% reduction in power and 22.05% increase in throughput. Our designs offer a throughput of upto 2 Gbps while reducing area and power by a maximum of 16.86% and 32.48% respectively. LiteHash functions also support the computation of the entire SHA-2 family of hash functions (SHA-224/256/384/512) with minor architectural modifications.
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来源期刊
ACM Transactions on Embedded Computing Systems
ACM Transactions on Embedded Computing Systems 工程技术-计算机:软件工程
CiteScore
3.70
自引率
0.00%
发文量
138
审稿时长
6 months
期刊介绍: The design of embedded computing systems, both the software and hardware, increasingly relies on sophisticated algorithms, analytical models, and methodologies. ACM Transactions on Embedded Computing Systems (TECS) aims to present the leading work relating to the analysis, design, behavior, and experience with embedded computing systems.
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