# Privacy-Preserving Data Analytics in Internet of Medical Things
## 3 Related Works  
This section reviews relevant research concerning privacy preservation in healthcare data. [PERSON] et al. [13] focused on the necessity of large datasets for the training robust deep learning models in healthcare while also acknowledging the privacy concerns and regulatory constraints that restrict data sharing in this field. To address these challenges, the authors highlighted the potential of federated learning to overcome these barriers by allowing data to remain with the local party (such as a hospital), ensuring confidentiality and compliance with data protection regulations. The authors specifically focused on two algorithms: Federated Averaging (FedAvg) and FedProx. Using federated learning in healthcare highlights several limitations; for example, privacy risks still exist, as model updates sent to a central server could be intercepted. Communication costs are also notable due to frequency of data exchanges between clients and the server. A hybrid strategy was presented by [PERSON] et al. [PERSON] et al. [PERSON] et al. (2018) and combined with a number of approaches to protect private patient data from breaches and unwanted access. This research methodology minimizes the impact on data utility while protecting privacy by integrating two key techniques: the FP-Growth algorithm for mining of frequent patterns and anonymization processes to conceal sensitive information. In order to solve privacy problems in healthcare big data, [PERSON] et al. [PERSON] et al. (2018) offered a novel system that combines Apache Spark with established anonymization approaches like K-anonymization and L-diversity. A notable development in the field is the integration of these techniques with Apache Spark, which offers excellent speed and efficiency for handling massive datasets. For the purpose of safeguarding local models in Internet of Things-based healthcare systems, [PERSON] et al. [PERSON] et al. (2018) suggested integrating homomorphic encryption with federated learning mechanisms. The model integrates data from many medical facilities, and each participant trains local models independently using their own data. Before the local models are aggregated, homomorphic encryption techniques are performed to safeguard the data. This stops possible adversaries from using inversion or model reconstruction attacks to deduce private information. [PERSON] et al. [PERSON] et al. (2018) thoroughly implemented an attribute-based access control model to protect electronic healthcare records (EHRs) in an XML-based system. In their model, sensitive data are partially encrypted by the system using XML encryption after access control. Next, the data are secured against unauthorized changes and access by utilizing XML digital signatures. Research by [PERSON] et al. [PERSON] et al. (2018) examined blockchain-based technology with the goal of improving the security and privacy of medical data. The approach focuses on decentralizing data storage through the use of blockchain technology, which lessens the vulnerabilities connected to centralized databases. It uses peer-to-peer (P2P) networks, where data are stored among numerous nodes. The massive volumes of data that are common in healthcare settings may make it difficult for the blockchain framework to scale effectively, which could result in longer transaction times and higher computational cost. [PERSON], [PERSON], et al. [PERSON] et al. (2018) implemented a machine learning approach using Extremely Randomized Trees (ERTs) that is specifically designed for health data with distributed structures. This distributed ERT technique modifies the traditional approach to adapt to a distributed setting, ensuring that data privacy is upheld by avoiding a direct data environment. Instead, data insights are derived through secure multi-party computation methods that allow entities to collaborate without exposing their private data. [PERSON], [PERSON], et al. [PERSON] et al. (2018) used the improved ElGamal and ResNet classifiers to maintaining privacy in a heart disease database.