支持用户追溯和轻量的共享云数据审计方案

发布时间：2018-10-18 10:45

【摘要】：在云计算中,数据通常由一组用户共享。由于第三方审计可以通过数据块的签名获取组成员的身份,为了保护群组成员的身份,现有对共享数据的公共审计方案都隐藏了组成员的身份。然而,身份的匿名性将导致一个组的成员可以恶意修改共享数据而不被发现,而且对于资源受限的设备,用户在产生签名的过程中计算量大。因此现有公共审计方案存在数据块身份的不可追溯、用户产生共享数据块签名的计算量大等问题。针对上述问题,提出了一种支持用户追溯和轻量的共享云数据审计方案(ASDA)。该方案利用安全中介者代替用户签名,保护了群组成员的身份,在签名的同时保存用户的信息,通过这些信息,可以追溯到数据块是由哪一个组成员修改,从而保证数据块身份可追溯性;而且利用新的数据块致盲技术,减少用户端计算量。实验结果表明,所提方案与利用第三方媒介存储共享云数据(SDVS)方案相比,减少了用户端计算时间,并且能够实现共享数据块身份的可追溯性。
[Abstract]:In cloud computing, data is usually shared by a group of users. Since third-party auditing can obtain the identity of group members through the signature of data blocks, in order to protect the identity of group members, the existing public audit schemes for shared data hide the identity of group members. However, the anonymity of identity will result in the members of a group can maliciously modify the shared data without being discovered, and for resource-constrained devices, the user will have a large amount of computation in the process of generating the signature. Therefore, the existing public audit schemes have problems such as the non-traceability of the identity of the data block, the large amount of computation of the user generating the shared data block signature, and so on. In order to solve the above problems, this paper proposes a shared cloud data audit scheme, (ASDA)., which supports user traceability and lightweight. The scheme uses security mediators to replace user signatures, which protects the identity of group members and saves user information while signing. Through this information, it can be traced back to which group member modified the block of data. Thus, the traceability of data block identity is ensured, and the new blind data block technology is used to reduce the computing load. The experimental results show that the proposed scheme can reduce the computing time of the client and realize the traceability of the identity of the shared data block compared with the scheme using third-party media to store shared cloud data.
【作者单位】：武汉科技大学计算机科学与技术学院;湖北省智能信息处理与实时工业系统重点实验室;
【基金】：国家自然科学基金资助项目(61303117,61602351)~~
【分类号】：TP309

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