Literature DB >> 26410315

Secure medical information sharing in cloud computing.

Zhiyi Shao1,2,3, Bo Yang1,2,3, Wenzheng Zhang2, Yi Zhao1, Zhenqiang Wu1, Meixia Miao4.   

Abstract

Medical information sharing is one of the most attractive applications of cloud computing, where searchable encryption is a fascinating solution for securely and conveniently sharing medical data among different medical organizers. However, almost all previous works are designed in symmetric key encryption environment. The only works in public key encryption do not support keyword trapdoor security, have long ciphertext related to the number of receivers, do not support receiver revocation without re-encrypting, and do not preserve the membership of receivers. In this paper, we propose a searchable encryption supporting multiple receivers for medical information sharing based on bilinear maps in public key encryption environment. In the proposed protocol, data owner stores only one copy of his encrypted file and its corresponding encrypted keywords on cloud for multiple designated receivers. The keyword ciphertext is significantly shorter and its length is constant without relation to the number of designated receivers, i.e., for n receivers the ciphertext length is only twice the element length in the group. Only the owner knows that with whom his data is shared, and the access to his data is still under control after having been put on the cloud. We formally prove the security of keyword ciphertext based on the intractability of Bilinear Diffie-Hellman problem and the keyword trapdoor based on Decisional Diffie-Hellman problem.

Keywords:  Medical information sharing; ciphertext security; multiple receivers; searchable encryption; trapdoor security

Mesh:

Year:  2015        PMID: 26410315     DOI: 10.3233/thc-150945

Source DB:  PubMed          Journal:  Technol Health Care        ISSN: 0928-7329            Impact factor:   1.285


  2 in total

1.  Analytical modeling and feasibility study of a multi-GPU cloud-based server (MGCS) framework for non-voxel-based dose calculations.

Authors:  J Neylon; Y Min; P Kupelian; D A Low; A Santhanam
Journal:  Int J Comput Assist Radiol Surg       Date:  2016-08-25       Impact factor: 2.924

2.  ReportFlow: an application for EEG visualization and reporting using cloud platform.

Authors:  S Bertuccio; G Tardiolo; F M Giambò; G Giuffrè; R Muratore; C Settimo; A Raffa; S Rigano; A Bramanti; N Muscarà; M C De Cola
Journal:  BMC Med Inform Decis Mak       Date:  2021-01-06       Impact factor: 2.796

  2 in total

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