{"title":"Integrating fully homomorphic encryption to enhance the security of blockchain applications","authors":"","doi":"10.1016/j.future.2024.07.015","DOIUrl":null,"url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":null,"pages":null},"PeriodicalIF":6.2000,"publicationDate":"2024-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Future Generation Computer Systems-The International Journal of Escience","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0167739X24003790","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, THEORY & METHODS","Score":null,"Total":0}
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.
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.