关于医药供应链安全和隐私问题的系统调查:分类、框架和研究挑战

Jigna J. Hathaliya, S. Tanwar
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引用次数: 0

摘要

几十年前,药品供应链(MSC)将药品从制造商转移到最终消费者手中,并将所有记录保存在人工登记簿中。MSC 和药品数据的人工中介管理往往会导致未经授权的第三方参与流程和非法篡改药品数据等问题。由于这种药品质量问题,最终用户得到的是假药,对患者的健康造成严重后果。随着时间的推移,人工数据管理和中介机构转变为实时跟踪、管理和交换数据的数字平台。实时数据交换为攻击者提供了可乘之机,他们可以瞄准 MSC,非法访问药品数据,修改药品条件、位置和规格。为此,本调查报告提出了安全和隐私问题,并讨论了安全解决方案。这种安全解决方案涉及各种数据安全和隐私框架,如微分割、零信任模型和许多其他基于软件的安全解决方案。此外,建议的调查还探讨了用于药品跟踪的射频识别,其中每个中介都通过互联网转换药品位置。而物联网则用于实时交换药品温度状况。此外,基于网络安全的解决方案有助于抵御恶意威胁,而区块链可保持数据的私密性和防伪性。人工智能提供了机器学习和深度学习模型,可用于分析大量数据,从 MSC 数据中产生洞察力。因此,本调查报告探讨了各种安全和隐私问题,并提供了有助于研究人员在这一领域开展工作的解决方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A systematic survey on security and privacy issues of medicine supply chain: Taxonomy, framework, and research challenges
Several decades ago, the medicine supply chain (MSC) transferred the medicines from the manufacturer to the end‐consumer and kept all records in a manual register. The manual intermediary management of MSC and medicine data often leads to issues like unauthorized third parties participating in the process and illegally tempering medicine data. As a result of this medicine temperament, end users get counterfeit medicine that poses severe consequences for patients' health. Over time, manual data management and intermediaries transform into digital platforms that track, manage, and exchange data in real‐time. Real‐time data exchange opens attackers up to target MSCs, access medicine data illegally, and modify medicine conditions, locations, and specifications. With the objective of this, the proposed survey identifies security and privacy issues and discusses security solutions. This security solution involves various data security and privacy frameworks such as micro‐segmentation, zero trust model, and many other software‐based security solutions. Moreover, The proposed survey explores radio frequency identification for medicine tracking in which each intermediary transforms the medicine location over the internet. In contrast, the Internet of Things is used to exchange medicine temperature conditions in real‐time. Furthermore, cybersecurity‐based solutions help protect against malicious threats, and blockchain keeps data private and temper‐proof. Artificial intelligence provides machine learning and deep learning models for analyzing large amounts of data to generate insights from the MSC data. Therefore, this survey addresses the various security and privacy issues and provides solutions that help researchers carry out work in this domain.
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