# Homomorphic Encryption Based Privacy-Preservation for IoMT
## 1 Introduction  
Intelligent healthcare systems in smart cities widely implement Internet of Things (IoT) sensors to monitor patient health. Sensor devices for humans such as smart thermometers record a patient's body temperature [1], Q-bands detect user mobility [2], pacemakers combined with medical alert systems monitor and alert during cardiac arrests [3], and proximity tracers identify potential new contagious disease clusters [4]. Temperature sensors monitor plasma storage in hospitals and are used for future studies [5]. These devices are some of the few medical sensors that monitor a citizen's health. Confidential patient information is ever at risk of being intercepted by attackers attempting to steal personal data and hold hospital and medical research institutions to ransom. Ransomware attacks on hospitals such as Universal Health led to doctors and nurses losing access to essential and critical patient data, requiring them to shift to pen and paper and address immediate concerns [6]. More than 250 hospitals and clinics in the USA were affected, resulting in delayed responses involving critical machine tasks such as blood pressure, oxygen level, and heart rate monitoring [7]. Furthermore, private data processes on public servers are exposed to increasing ransomware attacks, endangering patient healthcare services and risking data loss. The importance of this study lies in securing the data generated by medical devices and maintaining the privacy of data computations performed by healthcare applications in public servers. Healthcare systems require quick decision-making for high-risk patients suffering from after-effects of medical diagnosis. However, several existing studies rely on performing calculations on the cloud layer due to the vast amount of available computational and storage resources. Latency increases in transmitting results from the Cloud back to devices delays diagnosis for patients where quick decision making is essential for theirhealth [8; 9]. As medical data increases, communication overhead between the Cloud-IoT channel grows due to the frequent and large amount of medical data processing and data exchange in healthcare services [10]. Data analysis at the edge layer, which includes base stations, brings computation closer to the device layer, such as the IoMT reducing latency, bandwidth, and network delay to support real-time applications. Moreover, network bottlenecks and congestions formed during the long transmission path from the device layer to the Cloud layer are avoided using Multi-Access Edge Computing (MAEC) [11]. Several security vulnerabilities are found in the cloud layer, exposing data to intruders via cyber-attacks such as ransomware attacks, resulting in data alteration and manipulation. Furthermore, data analysis in the Cloud requires computation in servers belonging to third-party developers and organizations. Information collected is sold on the dark web to third-party vendors for marketing and advertising organizations. Other malicious users buy data for initiating scams, fraudulent advertisements, and holding data for ransom [12]. Encrypting data using homomorphic encryption prevents attackers from reading private medical data. Moreover, it does not require data decryption at the server-side for data analysis, preventing third-party entities from accessing information shared by Internet of Medical Things (IoMT) devices [13]. Secret sharing is ideal for securely and privately performing computations on untrusted Cloud servers where a user or an intelligent healthcare application does not have control over the privacy of the data [14]. The motivation for writing this paper is to address growing concerns of cyberattacks on healthcare networks. The proposed system resolves two significant problems in IoMT data security: (1) Data integrity and privacy preservation of private IoMT data are maintained when communicating on the network; (2) Computational operations on encrypted data on untrusted cloud servers do not expose patient data.