# Edge Computing in Healthcare: Innovations, Opportunities, and Challenges
## 4 Literature Review
### Privacy and Security
[37] introduce F-Classify, a novel privacy-preserving model using fuzzy logic to classify quasi-identifiers and Multiple Sensitive Attributes (MSAs). This solution addresses the inefficiency of traditional privacy preservation methods due to attribute correlation and focuses on multi-dimensional partitioning and fuzzy logic for categorization and anonymization. [PERSON] et al. [38] discuss the issue of managing stress through intelligent monitoring of sleep using Internet of Medical Things (IoMT) devices. The solution uses blockchain to securely record and manage data exchanges across IoMT devices, employing complex encryption methods to protect data integrity and confidentiality. An authorization mechanism ensures that personal health data are protected against unauthorized access. [PERSON] et al. [39] tackle the complex challenge of managing health data across different computing environments, edge -fog-cloud, while ensuring compliance with strict healthcare regulations and maintaining data privacy. The solution involves an architectural model that utilizes microservices and nanoservices, facilitating a seamless edge-to-fog-to-cloud data processing structure. [PERSON] et al. [2018] highlight the importance of enhancing data privacy in healthcare systems where sensitive information is frequently exchanged. The proposed solution leverages FL integrated with the latest 5G technologies, including network slicing. This setup ensures that data are processed locally at the edge and only model insights are shared network-wide, substantially reducing the risk of exposing sensitive health data. [PERSON] et al. [2018] address the critical need for robust security and privacy in IoT applications, where the vast volume of data generated by IoT devices is susceptible to numerous security threats. The primary challenge discussed is ensuring data security and integrity in IoT systems and a novel solution combining deep learning (DL) with blockchain technology is proposed. Deep learning algorithms are employed for efficient analytics and feature extraction, boosting the system's ability to handle large datasets effectively and blockchain is integrated to offer secure data transactions. A serverless privacy edge intelligence-enabled federated learning (SPEI-FL) framework is discussed in [2018], designed to address privacy and security issues found in current healthcare systems. The solution addresses scenarios in which data collection devices are portable, and patients might move between locations and networks, by using a six-way authentication process, based on the traditional four-way handshake with additional information about the geographical location and unique service provider id. It ensures that only verified patients are using the system, securing the system against possible attackers. The framework supports both structured and unstructured data, using an adaptable CNN model. Privacy protection is ensured by employing differential privacy techniques. [PERSON] et al. [2018] discuss different types of cyber threats, such as remote hijacking, DoS attacks, man-in-the-middle attacks, and impersonation, that compromise sensitive medical data in IoMT environments. The solution offered is a hybrid algorithm based on Deep Recurrent Neural Networks (DRNNs) and Supervised Machine Learning (KNN, decision tree, and Random Forest) to improve cyber-attack detection. The feature selection has been optimized using a Particle Swarm Optimization (PSO) algorithm. [PERSON] et al. [2018] propose a solution that combines FL with deep learning (DL) techniques to improve security on IoT devices. DL algorithms are used for anomaly detection and pattern recognition for possible security threats, while FL enhances data privacy. [PERSON] et al. [2018] tackle the challenge of anomaly detection in healthcare environments by proposing the AnoFed framework, which integrates transformer-based autoencoders (AEs) and variational autoencoders (VAEs) with Support Vector Data Description (SVDD) in an FL environment.