一种增强云环境下数据安全的新模型

IF 0.6 Q4 COMPUTER SCIENCE, THEORY & METHODS
G. Verma, Soumen Kanrar
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引用次数: 2

摘要

如今,云计算提供了一种新的计算范式。尽管云计算有一些好处,但确保云上敏感信息的保密性和完整性仍然是一个巨大的挑战。因此,为了在不损失任何敏感信息和隐私的情况下应对这些挑战,我们提出了一种新颖而强大的模型,称为“使用超椭圆曲线和生物识别技术增强云安全”(ECSHB)。ECSHB模型确保了云环境中数据的安全性、隐私性和身份验证。该方法结合了生物识别技术和超椭圆曲线加密技术(HECC),提高了云数据访问和资源保存的安全性。ECSHB使用更少的处理能力提供了高水平的安全性,这将自动降低总体成本。ECSHB的有效性以识别率、生物特征相似性评分、错误匹配率(FMR)和错误不匹配率(FNMR)的形式进行评估。ECSHB在保密性方面使用安全威胁模型分析进行了验证。同时还考虑了碰撞攻击、重放攻击和不可抵赖性的措施。结果的证据与现有的一些工作进行了比较,得到的结果在云环境下的数据安全和隐私方面表现出更好的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A novel model to enhance the data security in cloud environment
Nowadays cloud computing has given a new paradigm of computing. Despite several benefits of cloud computing there is still a big challenge of ensuring confidentiality and integrity for sensitive information on the cloud. Therefore to address these challenges without loss of any sensitive information and privacy, we present a novel and robust model called ‘Enhanced Cloud Security using Hyper Elliptic Curve and Biometric’ (ECSHB). The model ECSHB ensures the preservation of data security, privacy, and authentication of data in a cloud environment. The proposed approach combines biometric and hyperelliptic curve cryptography (HECC) techniques to elevate the security of data accessing and resource preservations in the cloud. ECSHB provides a high level of security using less processing power, which will automatically reduce the overall cost. The efficacy of the ECSHB has been evaluated in the form of recognition rate, biometric similarity score, False Matching Ratio (FMR), and False NonMatching Ratio (FNMR). ECSHB has been validated using security threat model analysis in terms of confidentiality. The measure of collision attack, replay attack and non-repudiation is also considered in this work. The evidence of results is compared with some existing work, and the results obtained exhibit better performance in terms of data security and privacy in the cloud environment.
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来源期刊
Multiagent and Grid Systems
Multiagent and Grid Systems COMPUTER SCIENCE, THEORY & METHODS-
CiteScore
1.50
自引率
0.00%
发文量
13
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