Integrating fully homomorphic encryption to enhance the security of blockchain applications

IF 6.2 2区 计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS
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引用次数: 0

Abstract

Blockchain has been widely used for secure transactions among untrusted parties, but the current design of blockchain does not provide sufficient privacy and security for the data on the chain, limiting its application in sensitive information scenarios. To address this problem, we propose integrating fully homomorphic encryption (FHE) to enhance the security of blockchain applications, which can extend the application scope of blockchain and improve the privacy and security of blockchain by the features of FHE. Our scheme classifies FHE into those supporting polynomial and non-polynomial operations and introduces the concept of ciphertext computation conversion into Ethereum, enabling conversion between different ciphertext computation types. Moreover, we analyse the security and correctness to explain the feasibility and availability of the scheme. We carry out comparative experiments using different open-source libraries for fully homomorphic encryption and the time performance evaluation of the ciphertext computation conversion under different thread counts. The experiment results demonstrate the efficiency and usability of our scheme.

整合全同态加密技术,提高区块链应用的安全性
区块链已被广泛应用于不受信任方之间的安全交易,但目前的区块链设计无法为链上数据提供足够的隐私和安全性,限制了其在敏感信息场景中的应用。针对这一问题,我们提出整合全同态加密(FHE)来增强区块链应用的安全性,通过FHE的特性扩展区块链的应用范围,提高区块链的隐私性和安全性。我们的方案将FHE分为支持多项式运算和非多项式运算的FHE,并在以太坊中引入了密文计算转换的概念,实现了不同密文计算类型之间的转换。此外,我们还分析了安全性和正确性,以解释该方案的可行性和可用性。我们使用不同的开源库进行了全同态加密的对比实验,并对不同线程数下的密文计算转换进行了时间性能评估。实验结果证明了我们方案的高效性和可用性。
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来源期刊
CiteScore
19.90
自引率
2.70%
发文量
376
审稿时长
10.6 months
期刊介绍: Computing infrastructures and systems are constantly evolving, resulting in increasingly complex and collaborative scientific applications. To cope with these advancements, there is a growing need for collaborative tools that can effectively map, control, and execute these applications. Furthermore, with the explosion of Big Data, there is a requirement for innovative methods and infrastructures to collect, analyze, and derive meaningful insights from the vast amount of data generated. This necessitates the integration of computational and storage capabilities, databases, sensors, and human collaboration. Future Generation Computer Systems aims to pioneer advancements in distributed systems, collaborative environments, high-performance computing, and Big Data analytics. It strives to stay at the forefront of developments in grids, clouds, and the Internet of Things (IoT) to effectively address the challenges posed by these wide-area, fully distributed sensing and computing systems.
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