# SoK: Delegated Security in the Internet of Things
## 1 Introduction  
The number of electronic devices in use is steadily increasing [1]. While IoT devices enhance comfort and are attractive for widespread utilisation, they also pose a significant cybersecurity risk [2]. During operation, IoT devices communicate with each other and with the outside world, receiving, transmitting and generating a large amount of data, which can be critical under certain conditions. Critical data in possession of adversaries can cause irreparable damage to individuals or institutions [3]. Beyond unauthorised data access, adversaries can also compromise IoT devices, leading to service disruptions and potential safety risks. For example, in healthcare applications, a compromised medical IoT device, e.g., a connected inhaler, blood sensor, or asthma monitor, could generate false alerts or misleading medical recommendations to patients and doctors, resulting in inappropriate treatment and endangering patient safety [4].  
Despite IoT devices providing a wide range of services, their computational and communication resources are constrained, as they are designed to be lightweight, and in many cases battery-powered. As a result, they are often unable to execute complex security mechanisms and protect themselves from cyberattacks [5]. One effective strategy to address this issue and ensure the security of resource-constrained IoT devices is delegation [6, 7].  
The general idea behind security delegation is that a resource-constrained device, which lacks the computational capacity to perform advanced security measures, outsources them to a more powerful device, called a _delegate_. The _delegate_ acts as a trusted entity thatperforms security-related functions on behalf of the constrained device, ensuring security and avoiding overloading the limited resources, thereby \"encapsulating\" the device in a secure environment (see Figure 1).  
For example, [PERSON] et al. propose a collaborative approach to key establishment based on delegation [8]. In the proposed scenario, a highly resource-constrained sensor intends to exchange data with an external server securely. Since the sensor is unable to perform costly computations, it delegates the establishment of a session key with the external server to a group of proxy nodes. The proxies complete key agreement with the server on behalf of the sensor using Diffie-Hellman key exchange and submit the resulting session key to the sensor. Thus, all heavy computations are performed by proxies, while the sensor only needs to handle the lightweight secret key. This approach results in lower costs compared to a TLS handshake. Additionally, one could argue that it shifts the adversary's target from the \"weak\" device to the \"strong\" one, which can be better protected. Hence, we can hope that delegation in IoT systems also enhances security. However, delegation may introduce other attack vectors and complicate security models as well as security properties due to the introduction of the delegate.  
Several studies have been carried out on security delegation, exploring different techniques and implementations. However, most works use different adversary models and assumptions [9; 10]. In some cases, solutions are even proposed without security arguments or proofs [11; 12; 13]. Some works focus more on performance improvements [14; 15], while others prioritise enhancing security [16; 17]. Furthermore, the role of the _delegate_ varies, with some approaches relying on centralised authorities [18; 19], while others distribute security tasks among multiple entities [8; 20]. The listed reasons make a direct comparison of delegating approaches challenging, a difficulty further exacerbated by the lack of a standardised evaluation framework.