论文标题
精确健康数据：数据安全和隐私的要求，挑战和现有技术
Precision Health Data: Requirements, Challenges and Existing Techniques for Data Security and Privacy
论文作者
论文摘要
精确健康利用各种来源的信息，包括OMIC，生活方式，环境，社交媒体，医疗记录和医疗保险索赔，以实现个性化的护理，预防和预测疾病以及精确的治疗方法。它广泛使用传感技术（例如电子健康监测设备），计算（例如机器学习）和通信（例如，健康数据中心之间的相互作用）。由于健康数据包含敏感的私人信息，包括患者和护理人员的身份以及患者的医疗状况，因此始终需要适当的护理。这些私人信息的泄漏会影响个人生活，包括欺凌，高保险费以及由于病史而导致的工作损失。因此，对信息的安全性，隐私和信任至关重要。此外，政府立法和道德委员会要求医疗保健数据的安全和隐私。根据精确的健康数据安全，隐私，道德和监管要求，在此中找到了利用健康数据的最佳方法和技术，因此精确的健康至关重要。在这方面，首先，本文探讨了世界各地的法规，道德准则以及特定领域的需求。然后它提出了要求并调查相关挑战。其次，本文研究了适合于精确健康数据计算的安全和隐私的机器学习方法及其在相关健康项目中的使用。最后，它通过概念系统模型说明了最佳的可用技术，可用于精确健康数据安全和隐私，该模型能够合规，道德清除，同意管理，医疗创新和健康领域的发展。
Precision health leverages information from various sources, including omics, lifestyle, environment, social media, medical records, and medical insurance claims to enable personalized care, prevent and predict illness, and precise treatments. It extensively uses sensing technologies (e.g., electronic health monitoring devices), computations (e.g., machine learning), and communication (e.g., interaction between the health data centers). As health data contain sensitive private information, including the identity of patient and carer and medical conditions of the patient, proper care is required at all times. Leakage of these private information affects the personal life, including bullying, high insurance premium, and loss of job due to the medical history. Thus, the security, privacy of and trust on the information are of utmost importance. Moreover, government legislation and ethics committees demand the security and privacy of healthcare data. Herein, in the light of precision health data security, privacy, ethical and regulatory requirements, finding the best methods and techniques for the utilization of the health data, and thus precision health is essential. In this regard, firstly, this paper explores the regulations, ethical guidelines around the world, and domain-specific needs. Then it presents the requirements and investigates the associated challenges. Secondly, this paper investigates secure and privacy-preserving machine learning methods suitable for the computation of precision health data along with their usage in relevant health projects. Finally, it illustrates the best available techniques for precision health data security and privacy with a conceptual system model that enables compliance, ethics clearance, consent management, medical innovations, and developments in the health domain.
