## 8 IoMT Security Model
### Privacy Model  
[PERSON] and [PERSON] [83] proposed a novel method to include a dual signature in the elliptic curve digital-signature algorithm (ECDSA) for IoMT systems. Using edge-computing servers, this system preserves the confidentiality of data transmitted from the IoMT to the cloud. In particular, the captured health data is hidden by the edge device, and the identity of the IoMT devices, namely wearable or smart devices, remains anonymous to the cloud. Since this solution is based on the elliptic-curve cryptography (ECC) approach, its implementation on IoMT devices was feasible and affordable. This technique confirms that, while the anonymity of the data source is ensured from the cloud perspective, the integrity and authentication of the origin of the collected data is also ensured. In addition, the computational requirements and complexity are minimized. [PERSON] et al. [84] proposed a reversible dual-frame data-hiding technique with high capacity for IoMT-based networks. Initially, the Huffman coding scheme was used to preprocess the captured secret data. A codebook of \"d\" bits is generated after the Huffman coding to encode the indices which are decimal values. For double steno-image acquisition, the value of the indices is divided into two parts and embedded in two images similar to each other. Although the scheme showed a very high payload, it maintained the perceptual quality at a high level. The scheme provides an average improvement in _embedability_ of 33.2%, with a signal-to-noise ratio (SNR) improvement of 1.32%. The average structural similarity index (SSIM) value is 0.8873. A significant improvement was offered by the system and was also computationally efficient, which allowed it to be used in the IoMT network. However, there was no effective strategy to control the underflow and overflow problems. [PERSON] et al. [85] proposed a practical system that reliably authenticates patients with noisy ECG signals and simultaneously provides differential privacy. With respect to the current motion status, the system can identify the motions and adapt the algorithm. By ensuring indistinguishability, the privacy of ECG patterns has been protected. This system preserves the speed of authentication by implementing lightweight online algorithms. On the other hand, it effectively disaggregates noise from ECG signals to ensure reliable authentication. It ensures indistinguishability via differential privacy to prevent adversaries from deducing patients' ECG information. This system also improves accuracy by applying soft thresholding while maintaining the claimed privacy guarantee. Online datasets were used to evaluate and validate the effectiveness of the system. In addition, a pilot study on human volunteers was conducted to validate the system. However, the system was not scalable enough for the attack. [PERSON] et al. [86] investigated an efficient blind-batch encryption scheme based on the Computational Diffie-Hellman hypothesis, which has been shown to be secure. For secure and privacy-preserving medical services in Smart Connected Health (SCH), the scheme used a protocol. With six classical attacks, the system analyzed the protocol and ran the prototype on the Intel Edison platform. Experiments revealed that the system was effective for \"cheap\" communication protocols and resource-constrained devices. For storage-limited devices, the system could require a high cost. In a recent work, [PERSON] and [PERSON] [2] consider the confidentiality issue for message exchange in the IoMT environment and propose SPMHF (Security and Privacy-aware Mobile Healthcare Framework). Alongside confidentiality/privacy, the mechanism ensures the integrity of the message, offers the strategy of audit control and ensures patient authentication, access control, data availability, transparency, and freshness of health data. To add more to these, the framework requires taking the patient's consent for allowing information exchange.