# Homomorphic Encryption Based Privacy-Preservation for IoMT
## 2 Related Works
### Existing Research
[21] presented a new privacy-enhanced data fusion strategy for encrypting data using homomorphic encryption and suggested an incentive-based approach to encourage users to share data with the healthcare network. The process combines mobile edge computing (M.E.C.) and IoMT, where various M.E.C. servers support COVID-19 applications, store, process, and analyze results. In place of using Cloud layer resources, all data computation is performed at the edge layer. [PERSON] et al. [22] proposed a fully homomorphic encryption scheme to perform neural network operations directly on floating-point data with reduced computational overhead. A vulnerability in the proposed Matrix Operation for Randomization or Encryption-based homomorphic encryption system is that the key is vulnerable to decryption. An attacker using an optimization problem with access to a large set of key pairs can determine the encryption key, resulting in data security and confidentiality issues. Raw data is uploaded to an external and private server on the Cloud for security, but the proposed scheme fails to protect it in transmission. [PERSON] et al. [23] presented a patient privacy protection model based on federated learning verification. The model combines homomorphic encryption, blockchain, and federated learning to ensure the privacy of medical records as data learning is performed both centrally and locally. Each client/user/device trains the model using their local dataset collected and transmitted to the central server. [PERSON] et al. [24] proposed a framework for medical image security. Firstly, the authors present a reversible data hiding scheme for image quality improvement and the embedding of privacy data in images. Secondly, medical image encryption is managed using a homomorphic encryption method using the chaotic map. Other research uses and relies on different encryption standards, such as the Elliptic Curve Digital Signature Algorithm (ECDSA) and technologies such as blockchains, to secure IoMT data. [PERSON] et al. [25] addressed the security and privacy concerns of IoMT data using the ECDSA to generate dual digital signatures. Data transmission from devices to the cloud layer via the edge is authenticated from valid sources using dual signatures. Signature verification is performed using edge layer resources due to their lack of resource constraints and agility compared to IoMT devices. The research objective is to prevent the edge from accessing the encrypted data and the Cloud from learning the user's identity. Data decrypted at the Cloud is exposed to external servers accessing data and other malicious cyber-attacks. [PERSON] et al. [26] proposed a decentralized architecture combining data sharing and offloading methods; the computation of data is performed at the M.E.C. for increased speed and user privacy. The novelty of this architecture lies in implementing smart contracts for authentication and traceability during data sharing. A central authority or entity requirement for approving a user access request to upload data is removed using smart contracts that establish the user's validity. Data integrity is maintained as information is stored in blocks, and any tampering will be reflected in modified hash values. Data lookup time is reduced due to the direct storing of hash values in smart contracts, providing direct data access in the Interplanetary File System.