Anonymous Aggregate Fine-Grained Cloud Data Verification System for Smart Health
Abstract: With the rapid development of cloud computing and Internet of Things (IoT), smart health (s-health) is anticipated to enhance healthcare quality significantly. However, data integrity, user anonymity, and authentication concerns have not been adequately addressed in s-health. Remote data integrity checking (RDIC) and digital signature schemes have great potential to address these requirements. Nevertheless, the direct adoption of these schemes suffers from two flaws. First, they incur prohibitively high computation and communication overhead. Second, they leak sensitive health information about patients and do not provide complete anonymity. To address these issues, we introduce $\mathbf {A^{3}B}$ - $\mathbf {RDV}$ , an aggregate anonymous attribute-based remote data verification scheme. In $\mathbf {A^{3}B}$ - $\mathbf {RDV}$ , the integrity of an arbitrary number of cloud data files can be verified at once without downloading the whole data, thereby saving communication and computation resources. Moreover, in $\mathbf {A^{3}B}$ - $\mathbf {RDV}$ , data owners can be authenticated by performing highly efficient operations. Also, $\mathbf {A^{3}B}$ - $\mathbf {RDV}$ provides complete anonymity and supports dishonest-user traceability. We provide security definitions for $\mathbf {A^{3}B}$ - $\mathbf {RDV}$ and prove its security under the hardness assumption of the bilinear Diffie-Hellman (BDH) problem. Performance comparisons and experimental results indicate that $\mathbf {A^{3}B}$ - $\mathbf {RDV}$ is more efficient and expressive than state-of-the-art approaches.
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