Privacy-preserving granular data retrieval indexes for outsourced cloud data

Zhigang Zhou, Hongli Zhang, Qiang Zhang, Yang Xu, Panpan Li
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引用次数: 9

Abstract

Storage as a service has become an important paradigm in cloud computing for its great flexibility and economic savings. Since data owners no longer physically possess the storage of their data, it also brings many new challenges for data security and management. Several techniques have been investigated, including encryption, as well as fine-grained access control for enabling such services. However, these techniques just expresses the "Yes or No" problem, that is, whether the user has permissions to access the corresponding data. In this paper, we investigate the issue of how to provide different granular information views for different users. Our mechanism first constructs the relationship between the keywords and data files based on a Galois connection. And then we exploit data retrieval indexes with variable threshold, where granular data retrieval service can be supported by adjusting the threshold for different users. Moreover, to prevent privacy disclosure, we propose a differentially privacy release scheme based on the proposed index technique. We prove the privacy-preserving guarantee of the proposed mechanism, and the extensive experiments further demonstrate the validity of the proposed mechanism.
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为外包云数据提供保护隐私的粒度数据检索索引
存储即服务因其巨大的灵活性和经济节约而成为云计算的一个重要范例。由于数据所有者不再拥有其数据的物理存储,这也给数据安全和管理带来了许多新的挑战。研究了几种技术,包括加密,以及用于启用此类服务的细粒度访问控制。然而,这些技术只是表达了“是或否”的问题,即用户是否具有访问相应数据的权限。在本文中,我们研究了如何为不同的用户提供不同的粒度信息视图。我们的机制首先基于伽罗瓦连接构建关键字和数据文件之间的关系。然后利用可变阈值的数据检索索引,通过调整不同用户的阈值来支持粒度数据检索服务。此外,为了防止隐私泄露,我们提出了一种基于所提出的索引技术的差分隐私释放方案。我们证明了所提出机制的隐私保护保证,大量的实验进一步证明了所提出机制的有效性。
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