Tree parity machine guided patients' privileged based secure sharing of electronic medical record: cybersecurity for telehealth during COVID-19.
Tree parity machine guided patients' privileged based secure sharing of electronic medical record: cybersecurity for telehealth during COVID-19.
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DOI：
10.1007/s11042-021-10705-6
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发表时间：
2021
影响因子：
3.6
通讯作者：
Sarkar M
中科院分区：
文献类型：
--
作者：
Sarkar A;Sarkar M
在2019冠状病毒病大流行中，远程医疗在电子医疗中发挥着重要作用。随着远程医疗使用的增加，电子医疗安全风险也显著上升。本文解决了电子医疗的关键问题之一，即安全性。秘密共享是一种确保可靠和安全访问信息的加密方法。为了消除现有秘密共享方案的局限性，提出了基于树奇偶校验机（TPM）的患者特权安全共享方案。这是一种新的秘密共享技术，它使用一个简单的基于掩码的操作来生成共享。这项工作考虑解决原始秘密共享方案中出现的挑战。该技术提高了现有方案的安全性。本研究引入了一个特权共享的概念，在k个共享中，一个共享应该来自特定的接收者（患者），该接收者被赋予特权来重建原始信息。在没有特权共享的情况下，原始信息不能被重构。该技术还提供基于TPM的秘密共享交换，以防止中间人攻击（MITM）。这里，两个神经网络接收共同的输入并交换它们的输出。在某些步骤中，它通过根据特定的学习规则设置离散权值来实现完全同步。此同步权重用作传输秘密共享的公共秘密会话密钥。实验结果表明，该方案具有较高的保护程度、可靠性和效率，可与现有的秘密共享方案相媲美。
In the COVID-19 pandemic, telehealth plays a significant role in the e-healthcare. E-health security risks have also risen significantly with the rise in the use of telehealth. This paper addresses one of e-health’s key concerns, namely security. Secret sharing is a cryptographic method to ensure reliable and secure access to information. To eliminate the constraint that in the existing secret sharing schemes, this paper presents Tree Parity Machine (TPM) guided patients’ privileged based secure sharing. This is a new secret sharing technique that generates the shares using a simple mask based operation. This work considers addressing the challenges presents in the original secret sharing scheme. This proposed technique enhances the security of the existing scheme. This research introduces a concept of privileged share in which among k number of shares one share should come from a specific recipient (patient) to whom a special privilege is given to recreate the original information. In the absence of this privileged share, the original information cannot be reconstructed. This technique also offers TPM based exchange of secret shares to prevent Man-In-The-Middle-Attack (MITM). Here, two neural networks receive common inputs and exchange their outputs. In some steps, it leads to full synchronization by setting the discrete weights according to the specific rule of learning. This synchronized weight is used as a common secret session key for transmitting the secret shares. The proposed method has been found to produce attractive results that show that the scheme achieves a great degree of protection, reliability, and efficiency and also comparable to the existing secret sharing scheme.
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