# Internet of Things-Based Healthcare Systems: An Overview of Privacy-Preserving Mechanisms
## 5 Literature Review
### Comparison with Existing Review Papers  
Several papers have explored privacy-preserving techniques in IoT-based healthcare, each differing in their focus, scope, and methodology. Below, we compare our study with key existing reviews and highlight how it expands on their findings.  
[PERSON] et al. [18] examined AI-driven privacy-preserving techniques, particularly machine learning-based anonymization and AI-enhanced access control. While insightful, their study lacked a broader classification of privacy-preserving mechanisms applicable to IoT healthcare. Our review goes beyond AI-based solutions, systematically categorizing various privacy techniques.  
[PERSON] et al. [19] focused on cybersecurity risks and resilience in IoT-based healthcare, using the CIA Triad (Confidentiality, Integrity, Availability) framework. However, their work did not classify privacy-preserving techniques or evaluate their effectiveness. In contrast, our review systematically organizes privacy-preserving solutions, assessing their real-world applicability and limitations.  
Figure 5: Mind map summarizing the key themes in the literature review on privacy in IoT-based healthcare.  
[PERSON] et al. [20] analyzed security and privacy in Healthcare 4.0, covering biometric security, blockchain, and cryptographic solutions. However, their focus was primarily on security rather than a structured classification of privacy solutions. Our review fills this gap by systematically categorizing privacy-preserving methods specific to IoT-based healthcare.  
[PERSON] et al. [21] provide a comprehensive review of the IoT in healthcare, covering technological advancements, security challenges, and market opportunities. Their study discusses various healthcare technologies and services but focuses primarily on general security models rather than privacy-preserving techniques. While they highlight potential risks and necessary security mechanisms, they do not offer a structured classification of privacy-preserving methods specific to IoT-based healthcare. Our study addresses this gap by systematically categorizing privacy-preserving techniques and evaluating their effectiveness in real-world applications, providing a more focused approach to privacy concerns in IoT healthcare systems.  
[PERSON] et al. [22] proposed Secure Multi-Party Computation (MPC) and homomorphic encryption (HE) as privacy-preserving mechanisms for secure data sharing and disease prediction in online healthcare systems. Their work demonstrated how encrypted data sharing can improve privacy, utilizing the Random Forest Algorithm for symptom-based disease prediction. However, their study primarily focused on encryption-based techniques and lacked a comparative evaluation of other privacy-preserving solutions, such as federated learning, blockchains, and differential privacy. In contrast, our study extends beyond encryption-based methods, systematically classifying a wide range of privacy-preserving approaches and assessing their security guarantees, implementation challenges, and scalability.  
Unlike previous work, our study systematically classifies privacy-preserving techniques in IoT-based healthcare, analyzing their functionality, security guarantees, and practical applications. This comparative approach provides a structured understanding of the existing solutions and their limitations. Additionally, we identify key research gaps and emerging challenges, offering insights for future advancements in privacy preservation.  
While previous papers have provided valuable insights into specific privacy-preserving techniques, they often focus on particular aspects, such as AI-driven solutions, blockchain security, or cybersecurity risks. In contrast, our review offers a broader classification and comparative analysis of privacy-preserving methods across different IoT healthcare applications. In the following sections, we further explore the integration of the IoT in healthcare, analyzing key challenges, privacy concerns, and potential solutions.