FUZZY IDENTITY-BASED DATA INTEGRITY AUDITING FOR DEPENDABLE CLOUD STORAGE SYSTEMS

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Abstract

Information honesty, a center security issue in solid distributed storage, has gotten a lot of consideration. Information inspecting conventions empower a verifier to productively check the trustworthiness of the re-appropriated information without downloading the information. A key exploration challenge related with existing plans of information reviewing conventions is the intricacy in key administration. In this paper, we look to address the unpredictable key administration challenge in cloud information uprightness checking by presenting fluffy personality based examining, the first in such a methodology, as far as we could possibly know. All the more explicitly, we present the crude of fluffy character based information examining, where a client's personality can be seen as a lot of spellbinding qualities. We formalize the framework model and the security model for this new crude. We at that point present a solid development of fluffy personality based inspecting convention by using biometrics as the fluffy character. The new convention offers the property of mistake resistance, in particular, it ties with private key to one personality which can be utilized to confirm the rightness of a reaction created with another character, if and just if the two characters are adequately close. We demonstrate the security of our convention dependent on the computational Diffie-Hellman suspicion and the discrete logarithm supposition in the particular ID security model. At long last, we build up a model usage of the convention which shows the common sense of the proposition.
基于模糊身份的可靠云存储系统数据完整性审计
信息诚实是固体分布式存储的核心安全问题,已引起人们的广泛关注。信息检查约定使验证者能够有效地检查重新占用的信息的可信度,而无需下载信息。与现有信息审查惯例计划相关的密钥探索挑战是密钥管理的复杂性。在本文中,我们希望通过提出基于蓬松个性的检查来解决云信息正确性检查中不可预测的密钥管理挑战,这是我们可能知道的第一个这样的方法。更明确的是,我们呈现了粗糙的基于角色的信息检查,其中客户的个性可以被视为许多引人入胜的品质。我们形式化了这种新原油的框架模型和安全模型。在此基础上,利用生物特征作为绒毛特征,提出了一种基于绒毛特征检测惯例的绒毛特征检测方法。新的约定提供了抗错误的特性,特别是,它将私钥与一个人格联系起来,如果并且仅仅如果两个角色足够接近,则可以用来确认与另一个角色产生的反应的正确性。我们证明了我们的约定的安全性依赖于计算Diffie-Hellman怀疑和特定ID安全模型中的离散对数假设。最后,我们建立了显示命题常识的约定的模型用法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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