# Internet of Things-Based Healthcare Systems: An Overview of Privacy-Preserving Mechanisms
## 5 Literature Review
### Privacy-Preserving Schemes  
In this section, we explore a variety of papers that put forth privacy-preserving schemes employing diverse techniques. [PERSON] et al. [35] proposed a privacy-preserving scheme using homomorphic encryption for securing medical data in the IoMT. The system ensures data integrity, privacy preservation, and secure computation on untrusted cloud servers. Key contributions include virtual node clusters, data encryption, and data confidentiality. A comparative analysis demonstrated the effectiveness of the proposed scheme in maintaining IoMT data security. [PERSON] et al. proposed M-PPKS [36], a privacy-preserving k-means strategy for healthcare systems using physical sensors. The strategy ensures participant confidentiality during data handling by dividing iterations into two stages: finding the nearest cluster centers and computing new centers. The approach achieved efficient clustering results while preserving privacy. The paper utilized the Paillier cryptosystem for data encryption and introduced a cloud platform to reduce the communication complexity. [PERSON] et al. [37] proposed ADDETECTOR, a privacy-preserving system for early-stage Alzheimer's disease detection using IoT devices. It collects user audio and employs novel linguistic features for accuracy. ADDETECTOR ensures privacy through a three-layer architecture, federated learning (FL), and differential privacy (DP) mechanisms. Experimental results showed high accuracy and easy deployment. [PERSON] et al. [38] proposed a privacy-preserving model for IoT healthcare using federated learning and Secure Multi-Party Computation. The model enables cooperative learning while keeping training data on the device, ensuring privacy. Algorithms like FedAvg, FedBCD, and FedProx were discussed for high-performance data analysis. The proposed model enhanced privacy and data sharing in the network. [PERSON] et al. [39] proposed a privacy-preserving data collection scheme for IoT-based healthcare. Their approach utilizes a clustering-based anonymity model to preventprivacy attacks and ensures privacy on both the client and server sides. The scheme effectively addresses various disclosure and attack scenarios. The paper introduced the discernibility Metric (DM) for evaluating the data quality, and the proposed scheme provides a comprehensive solution to privacy concerns in IoT-based healthcare systems. [PERSON] et al. [40] proposed a privacy-preserving scheme for IoT-based healthcare systems, ensuring patient location and data privacy. The scheme provides mutual authentication, patient anonymity, and authorized access to patient information. It is secure against attacks and offers additional security services with a minimal sensor overhead. Performance analysis showed improved efficiency compared to existing methods. The proposed scheme presents a comprehensive approach to preserving patient privacy in IoT healthcare systems. [PERSON] et al. [41] addressed privacy challenges in the IoT and proposed techniques for preserving privacy with different types of IoT data. Their methods included data stream anonymization using k-anonymity and privacy-enhancing techniques for continuous and media data. Experimental results demonstrated privacy preservation without compromising data utility. This paper offers a comprehensive overview of privacy-enhancing techniques for IoT data. [PERSON] et al. [42] discuss how federated learning enhances healthcare by enabling privacy-preserving collaborative data analysis for disease prediction and monitoring. They emphasize the capability of the decentralized nature of federated learning to protect sensitive patient information while addressing challenges and advocating for its integration into healthcare systems. [PERSON] et al. [18] explore privacy-preserving techniques in IoT-based cloud systems, focusing on strategies like encryption, anonymization, and access control.