Dataset Information

A searchable personal health records framework with fine-grained access control in cloud-fog computing.

ABSTRACT: Fog computing can extend cloud computing to the edge of the network so as to reduce latency and network congestion. However, existing encryption schemes were rarely used in fog environment, resulting in high computational and storage overhead. Aiming at the demands of local information for terminal device and the shortcomings of cloud computing framework in supporting mobile applications, by taking the hospital scene as an example, a searchable personal health records framework with fine-grained access control in cloud-fog computing is proposed. The proposed framework combines the attribute-based encryption (ABE) technology and search encryption (SE) technology to implement keyword search function and fine-grained access control ability. When keyword index and trapdoor match are successful, the cloud server provider only returns relevant search results to the user, thus achieving a more accurate search. At the same time, the scheme is multi-authority, and the key leakage problem is solved by dividing the user secret key distribution task. Moreover, in the proposed scheme, we securely outsource part of the encryption and decryption operations to the fog node. It is effective both in local resources and in resource-constrained mobile devices. Based on the decisional q-parallel bilinear Diffie-Hellman exponent (q-DBDHE) assumption and decisional bilinear Diffie-Hellman (DBDH) assumption, our scheme is proven to be secure. Simulation experiments show that our scheme is efficient in the cloud-fog environment.

SUBMITTER: Sun J

PROVIDER: S-EPMC6264141 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Publications

A searchable personal health records framework with fine-grained access control in cloud-fog computing.

Sun Jin J Wang Xiaojing X Wang Shangping S Ren Lili L

PloS one 20181129 11

Fog computing can extend cloud computing to the edge of the network so as to reduce latency and network congestion. However, existing encryption schemes were rarely used in fog environment, resulting in high computational and storage overhead. Aiming at the demands of local information for terminal device and the shortcomings of cloud computing framework in supporting mobile applications, by taking the hospital scene as an example, a searchable personal health records framework with fine-grained ...[more]

PMID: 30496194

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Project description:Presently, the focus of target detection is shifting towards the integration of information acquired from multiple sensors. When faced with a vast amount of data from various sensors, ensuring data security during transmission and storage in the cloud becomes a primary concern. Data files can be encrypted and stored in the cloud. When using data, the required data files can be returned through ciphertext retrieval, and then searchable encryption technology can be developed. However, the existing searchable encryption algorithms mainly ignore the data explosion problem in a cloud computing environment. The issue of authorised access under cloud computing has yet to be solved uniformly, resulting in a waste of computing power by data users when processing more and more data. Furthermore, to save computing resources, ECS (encrypted cloud storage) may only return a fragment of results in response to a search query, lacking a practical and universal verification mechanism. Therefore, this article proposes a lightweight, fine-grained searchable encryption scheme tailored to the cloud edge computing environment. We generate ciphertext and search trap gates for terminal devices based on bilinear pairs and introduce access policies to restrict ciphertext search permissions, which improves the efficiency of ciphertext generation and retrieval. This scheme allows for encryption and trapdoor calculation generation on auxiliary terminal devices, with complex calculations carried out on edge devices. The resulting method ensures secure data access, fast search in multi-sensor network tracking, and accelerates computing speed while maintaining data security. Ultimately, experimental comparisons and analyses demonstrate that the proposed method improves data retrieval efficiency by approximately 62%, reduces the storage overhead of the public key, ciphertext index, and verifiable searchable ciphertext by half, and effectively mitigates delays in data transmission and computation processes.

Dataset Information

A searchable personal health records framework with fine-grained access control in cloud-fog computing.

Publications

A searchable personal health records framework with fine-grained access control in cloud-fog computing.

Similar Datasets

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