# Edge Computing in Healthcare: Innovations, Opportunities, and Challenges
## 4 Literature Review
### Privacy and Security  
In the context of edge computing, data privacy and requirements are important in ensuring that user personal and sensitive information is handled securely and following well-established guidelines. This is especially important in the context of healthcare applications in the IoT, where user information is processed. Trust is obtained by ensuring data integrity is mandatory, by enforcing that the personal information remains reliable and unmodified during storage and data transmission. Robust access control mechanisms are necessary to ensure that only authorized users or systems can access their data. The principle of data minimization is important to reduce privacy risks and to ensure that only relevant and necessary information is collected and processed for the intended use. The development of edge computing technologies should involve regulations to ensure user privacy and legal compliance. These regulations depend on the region the application is being used in. The most common examples are the General Data Protection Regulation (GDPR), the California Consumer Privacy Act (CCPA), and the Health Insurance Portability and Accountability Act (HIPAA). Various countries have data localization laws requiring that certain data be stored and processed within their territory. Security in healthcare technology incorporates a wide range of concerns, protecting IoMT devices from cyber threats, ensuring secure communications, maintaining data integrity across distributed networks, and safeguarding electronic health records (EHRs). The increasing adoption of IoMT devices has come with a huge amount of data exchange between the IoMT and healthcare systems, which exposes them to various cyber threats such as remote hijacking, denial of service, data impersonation, etc. As can be seen in Figure 9, different security and privacy requirements need to be addressed through various innovative techniques to achieve the vision of edge-enabled healthcare. Figure 8: Paper distribution on three research areas. A novel authentication method using physiological signals, Heart Rate Variability (HRV), from smart edge devices is presented by [PERSON] et al. (2018). The proposed solution uses classification algorithms (K-Nearest Neighbor, Random Forest, Multi-Layer Perceptron, and Naive Bayes) as possible solutions to maintain user privacy but indicates that the device type and the sensors used play an important role in data quality and the overall accuracy of this method. [PERSON] et al. (2019) describe a solution that handles the issues of sharing EHRs between smart devices securely and efficiently. The proposed scheme combines attribute-based encryption with blockchain to ensure data privacy, confidentiality, anti-collusion, and fairness. The main algorithms used are GlobalSetup and AuthoritySetup, combined with online-offline encryption and policy hiding. [PERSON] et al. (2019) propose EdgeHR, a blockchain-based electronic health record (EHR) system that addresses the current limitations of using public blockchain for health data storage and management. To solve the issues related to cost and lack of performance in current healthcare blockchain solutions, the authors propose a hybrid storage solution, where medical records are stored locally, on users' devices, while the blockchain is used to trace these records, ensuring security and accessibility. By decentralizing storage and control, this solution enhances privacy and reduces the risk of unauthorized access and improves the constraints of storage by improving the use of smart contracts and design patterns to efficiently manage patients' data. [PERSON] et al. (2018) employ edge computing and blockchain technologies for implementing local consortium blockchains for authentication, device management, and personal data storage. The authors use smart contracts for access control policies and to ensure secure data logging. Current issues related to privacy and security in IoT systems are discussed by [PERSON] et al. (2018).