# Homomorphic Encryption Based Privacy-Preservation for IoMT
## 4 Discussion  
In this section, we analyze the security of the proposed scheme, and discuss comparison results with other existing research. The four key areas of consideration, data confidentiality, integrity, privacy-preservation, and secure data computation, are essential to secure and preserve the privacy of IoMT data. We observe from the summary in Table 1 that existing research either do not or only partially satisfy the key areas of consideration for a complete privacy-preserving scheme for securing IoMT data in a healthcare system. Implementation of homomorphic encryption [21; 22; 23; 24] ensures data privacy stored in local edge nodes and cloud environments. [PERSON] et al. [21] performed a hypothesis test to ascertain the reliability of collected data and encrypts each set of grouped information for user privacy at the data fusion center. Their research is focused on ensuring the reliability of data and achieving privacy of patients using homomorphic encryption. Computations of encrypted data are performed at local physical datacenters where mathematical operations are exposed to external entities. The vital challenge of masking operations from untrusted entities is not addressed. [PERSON] et al. [22] used keys to encrypt data using linear functions. However, an attacker exposes the keys using an extensive database of values associated with encrypted keys. Evaluation results show that the ciphertext data used for computation is exposed as plaintext using an optimization problem. Similarly, in [23; 24; 25; 26], the objective of the research and evaluation results focuses on encrypting data for privacy but does not provide masking for computation operations from untrusted service providers such as cloud operators, IoT devices and mobile edge datacenters. Our proposed scheme benefits from secret nodes at the local edge layer, preventing untrusted cloud providers from learning the computation operations. Keys generated using secret sharing prevents an attacker from reconstructing distributed data among several VENs and learning the final key used to create shares. Furthermore, existing research does not address the lack of a secure communication channel between the device and base station. Data reliability in our scheme is preserved and ensured using the timestamp-based clock synchronization method. The advantage of the proposed scheme compared with existing research is based on its complete security of data, starting from when devices share it with the local base station. Data storage and integrity are maintained using homomorphic encryption, and finally, operation privacy and security are ensured using secret sharing and VENs. Medical data requires constant computation using various artificial intelligence methods to analyze and provide output to intelligent healthcare systems. Exposure of computation operations exposes data to untrusted cloud servers, allowing possible deciphering of the type of data stored in Cloud. Third-party cloud services often rely on selling user data to further untrusted parties that use it for their marketing operations or to promote fraudulent schemes and advertisements. Schemes performing computation operations on local IoT devices based on a distributed manner rely on sensor devices with weak inbuilt security protocols. Cyberattacks such as botnet attacks expose operations to malicious parties. It is essential to perform operations directly on network nodes that are maintained by healthcare services and are closer to the device layer for reduced latency. The proposed scheme performs operations on the edge layer using VENs. In this paper, we assume that the healthcare network consumes cloud services for increased computational power at the edge layer. Multiple VENs are designed surrounding the hospital and are distributed with an equal set of computational power to prevent a cloud service from determining which node was used to perform mathematical operations.