利用区块链授权和数据索引与分析的用户认证 IoMT 安全模型

Y. Jani, P. Raajan
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引用次数: 0

摘要

医疗物联网(IoMT)可大大提升传统的医疗保健(HC)服务。为了确保医疗保健数据的安全,人们开发了许多数据保护和身份验证技术。但是,这些技术无法有效解决安全问题,也无法在最短时间内检索数据。因此,这项工作提出了一个用户认证安全框架,该框架基于区块链授权,使用编码访问策略和带有数据索引的智能合约。首先,为了预约,患者需要注册并登录服务器。就诊后,数据被感知并转换成密码。该密码经过加密后上传到医院云服务器。在美国信息交换标准码(ASCII)二进制索引树中,数据的位置被索引。同时,根据诊疗细节创建智能合约,将其转换为哈希码并存储在区块链中。然后,利用病人和医生的数据属性,创建一个编码访问策略。现在,医生登录服务器,创建智能合约,并将其转换为哈希代码。以智能合约和编码策略为基础,执行基于区块链的授权。验证后,借助索引树检索数据。最后,为了提供处方,使用 Sigmoid Swish Long short-term memory(SS-LSTM)分析解密数据的属性。在实验评估中,拟议机制的性能以优异的结果得到了证明。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

User-authenticated IoMT security model using blockchain authorization with data indexing and analysis

User-authenticated IoMT security model using blockchain authorization with data indexing and analysis

The Internet of Medical Things (IoMT) could significantly enhance conventional Healthcare (HC) services. To secure HC data, numerous data preservation and authentication techniques were developed. But, they could not effectively address security concerns and failed to retrieve data in minimal time. Thus, this work proposes a user Authenticated Security framework with blockchain-based authorization using an encoded access policy and smart contract with data indexing. Primarily, to book an appointment, the patient registers and login to the server. After consultation, the data is sensed and converted into cipher. This cipher is encrypted and uploaded to the hospital cloud server. In American Standard Code for Information Interchange(ASCII) binary Indexed Tree, the data’s location is indexed. In the meantime, a smart contract is created grounded on consultation details, which are converted to hashcode and stored in the blockchain. Afterward, by utilizing the patient’s and doctor’s data attributes, an encoded access policy is created. Now, the doctor login to the server, and a smart contract is created, which is converted to hash code. Grounded on the smart contract and encoded policy, blockchain-based authorization is performed. After verifying, the data is retrieved with the help of the indexed tree. Lastly, to provide a prescription, the attributes of the decrypted data are analyzed using Sigmoid Swish Long short-term memory (SS-LSTM). In experimental assessment, the proposed mechanism’s performance is proven with superior outcomes.

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