# Privacy-Preserving Data Analytics in Internet of Medical Things
## 3 Related Works
In their model, patients use wearable devices, and sensors connected to these devices gather data and transfer them to a microprocessor; the collected data are then sent to the cloud. The upgraded ElGamal encryption technique is used by the trusted cloud to safely protect patient data from outside threats. To accurately predict whether a patient is suffering with heart disease or not, a CNN Classifier with ResNet-50 is employed for data categorization and refinement. However, key generation and encryption add to the computational cost, and implementation depends on a Trusted Authority (TA). Research by [PERSON], [PERSON], et al. [PERSON] et al. (2018) outlined a novel searchable encryption (SE) scheme designed for IoT-enabled healthcare systems, focusing on forward privacy and verifiability. Searchable encryption allows encrypted data to be searched by authorized users without first decrypting them. Forward privacy ensures that updates to the dataset do not reveal any information about the contents of past search queries, thereby enhancing the security of dynamically changing databases like those found in healthcare systems. The solution proposed by [PERSON] et al. improves upon these by incorporating a trapdoor permutation function, ensuring that newly inserted records do not compromise the privacy of previously performed searches. Furthermore [PERSON], [PERSON], et al. [22] described a methodology that combines Federated Learning (FL) with Physical Layer Security (PLS) to enhance the privacy and efficiency in medical records. FL is employed to train local models at various nodes without sharing the unprocessed data among them. Only model parameters are shared with a central server or amongst nodes, significantly reducing the risk of exposing sensitive health data. Another approach that [PERSON], [PERSON], et al. [23] described uses cloud computing to facilitate the distribution of a Hierarchical Long Short-Term Memory (HLSTM) architecture among distributed dew servers. Before the data are utilized to train the model, they are preprocessed to assure the quality of IoMT devices. The complex series of events in the IoMT data flow is intended to be handled by the HLSTM architecture. In order to preserve the integrity of hierarchical data structures, it makes use of a two-layered LSTM network in which the first layer creates phrase vectors and the second layer collects these into a document vector. Federated learning is used in the intrusion detection model, which forms the basis of the methodology. Subsets of the data are used to train local models on dew servers, which subsequently feed into the creation of a global model. [PERSON], [PERSON], et al. [24] implemented a Modular Encryption Standard (MES) to secure health data in Mobile Cloud Computing (MCC) environments. Health data are categorized and recognized according to their sensitivity before encryption. Several encryption modules are employed at different stages of the multi-layered encryption method used by the MES technique. This approach ensures that data are treated in accordance with their security classification at every stage, starting with the user's mobile device and continuing to the cloud. [PERSON] et al. [25] explored an innovative way to maintain privacy in predictive healthcare analytics by utilizing Mosaic Gradient Perturbation (MGP) technology. Based on differential privacy, the concept aims to preserve model correctness while reducing the danger of model inversion attacks. The MGP method is intended to cause more of a perturbation for the gradient parts of the objective function linked to sensitive characteristics than for non-sensitive characteristics. Furthermore, the difficulties of accomplishing searchable and privacy-preserving data exchange in cloud-assisted electronic health environments were examined by [PERSON] et al. [26]. The suggested system makes use of modern cryptographic algorithms to facilitate effective, private data sharing and searches.