Literature DB >> 34329173

Efficient, Revocable, and Privacy-Preserving Fine-Grained Data Sharing With Keyword Search for the Cloud-Assisted Medical IoT System.

Yangyang Bao, Weidong Qiu, Peng Tang, Xiaochun Cheng.   

Abstract

The cloud-assisted medical Internet of Things (MIoT) has played a revolutionary role in promoting the quality of public medical services. However, the practical deployment of cloud-assisted MIoT in an open healthcare scenario raises the concern on data security and user's privacy. Despite endeavors by academic and industrial community to eliminate this concern by cryptographic methods, resource-constrained devices in MIoT may be subject to the heavy computational overheads of cryptographic computations. To address this issue, this paper proposes an efficient, revocable, privacy-preserving fine-grained data sharing with keyword search (ERPF-DS-KS) scheme, which realizes the efficient and fine-grained access control and ciphertext keyword search, and enables the flexible indirect revocation to malicious data users. A pseudo identity-based signature mechanism is designed to provide the data authenticity. We analyze the security properties of our proposed scheme, and via the theoretical comparison and experimental results we demonstrate that for the resource-constrained devices in the patient and doctor side of MIoT, in comparison with other related schemes, ERPF-DS-KS just consumes the lightweight and constant size communication/storage as well as computational time cost. For the keyword search, compared with related schemes, the cloud can quickly check whether a ciphertext contains the specified keyword with slight computations in the online phase. This further demonstrates that ERPF-DS-KS is efficient and practical in the cloud-assisted MIoT scenario.

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Year:  2022        PMID: 34329173     DOI: 10.1109/JBHI.2021.3100871

Source DB:  PubMed          Journal:  IEEE J Biomed Health Inform        ISSN: 2168-2194            Impact factor:   5.772


  1 in total

1.  An Analysis of Image Features Extracted by CNNs to Design Classification Models for COVID-19 and Non-COVID-19.

Authors:  Arthur A M Teodoro; Douglas H Silva; Muhammad Saadi; Ogobuchi D Okey; Renata L Rosa; Sattam Al Otaibi; Demóstenes Z Rodríguez
Journal:  J Signal Process Syst       Date:  2021-11-08
  1 in total

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