## 1 Introduction  
The internet and the Internet of Things are the biggest boons to civilization in today's world. Both of these methods allow authenticated users and their owners to access their data remotely. Based on this, the IoT is used in the healthcare sector for the close monitoring of patients by doctors. In healthcare IoT, security is a must because it saves confidential data in the cloud. Traditional encryption algorithms necessitate the use of an access key at all times to access the data. This provides greater security, but in an emergency, the data cannot be accessed immediately. To overcome this, the break-glass concept was introduced in healthcare IoT. In the break-glass (BG) method, the secured data can be accessed only once without any login credentials. [1] This helps in an emergency, but provoking BG is an important task. In this section, the techniques used in healthcare IoT for security are discussed. A survey on techniques used for securing the data in the Internet of Things is discussed in [2]. The four-phase authentication process is proposed between the server and local processing unit for securing the data transmission in the body sensor network [3]. A Datagram transport layer security handshake (DTLS-H) mechanism and session-oriented data access scheme are proposed in [4]. Here, the DTLS-H is used to create the authentication and data transmission process between the users in fog computing-based healthcare IoT. This approach is better in all terms such as security, computation, and storage overhead in data transferring between users. A survey of security mechanisms used for various applications in the IoT domain is discussed in [5]. Most of the existing methods in IoT utilized the two-stage authentication scheme. This approach is not sufficient for third-party storage data. To overcome this, [6] proposed a biometric-based authentication scheme for each layer of the IoT process. Here, they utilized a face recognition scheme for data access in four layers of the IoT process. They combined face recognition with bilinear cryptography to prevent data leakage in each layer. This approach provides high security compared to traditional approaches. However, the authenticated user also cannot access data when they face any accidents or injuries. A session-oriented hash lock and key-based communication access protocol are proposed in [7]. Here, two types of keys are generated during the communication between devices: the random key and the session key. The random key is used for authentication and the session key is used for authenticating the data transmission and analyzing the presence of intruders in the communication. The hash lock scheme is used to secure the data during the transmission process. A survey of smart healthcare management using wearable sensors and IoT is presented in [8]. Here, smart healthcare architecture, communication protocols, and security issues are discussed. Based on their survey, the modifications in attribute-based encryption and fully homomorphic encryption are suitable for improving security in IoT. An end-to-end security mechanism is proposed for Internet of Things applications [9]. Here, the authentication is performed using the Advanced Encryption Standard Galois Counter Mode (AES-GCM) 256-bit and one-pass scheme. The authentication between the base station and users is performed using hash key generation. A Secure Authentication and Prescription Safety protocol is proposed for healthcare IoT [10]. Here, efficient elliptical cryptography is proposed for securing data transmission between all parties, such as nurses, doctors, and patients. This approach performed well against the attacks, but the symmetric key system with single cryptography results in data leakage during third-party storage. A survey on data access mechanisms and attacks in the healthcare environment is discussed in [11]. Based on that, a secured scheme is proposed for both the cloud and healthcare local data. The Shamir secret sharing scheme is used in [12] to protect electronic health records.