# Towards Crowdsourcing Internet of Things (Crowd-IoT): Architectures, Security and Applications
## 4 Trustworthy, Security and Privacy in Crowdsourcing Internet of Things
### Security in Crowd-IoT
These approaches can benefit resource-constrained and battery-limited devices in IoT and the next generation 5G communication. A group of researchers studied the potential to combine crowdsourcing, IoT, and 5G. Considering 5G IoT is the ecosystem formed by 5G together with IoT, the authors in [55] analyzed the idea of crowdsourcing analysis against cyberattacks in 5G IoT. They started to analyze some use cases in crowdsourcing focusing on security. The potential areas of a 5G ecosystem where crowdsourcing can stop or mitigate the effect of local and remote attacks and discourage cybercriminal activities and behavior is highlighted. They also studied how these can be applied at different levels involving crowds which can be service providers, infrastructure, and users. Trusting 5G to solve IoT security issues or that IoT devices are more secure with full deployment of 5G may not be correct. Cooperation mechanisms are needed so that the IoT device data can be used for the detection and mitigation of threats. Figure 17: Architecture for crowdsourced industrial IoT [53]. There are few researchers working on the security aspects of fog-based crowdsourcing. The authors [33] proposed a fog-based spatial crowdsourcing (Fo-DSC) framework for task assignment and secure data deduplication. Figure 18 shows the system model of Fo-DSC, which contains three layers: (1) Service layer, (2) Fog layer, and (3) Mobile user layer. The spatial crowdsourcing (SC) server assigns the tasks to the fog nodes in the sensing vicinity. The fog nodes then distribute the task to users based on the task requirements from SC-server. There are six phases in the Fo-DSC framework: Setup, T-Releasing, T-Allocation, DCollection, D-Ddeduplication, and D-Reading. For secure data deduplication, Fo-DSC framework allows fog nodes for data deduplication without disclosing any information about the reports. Message-lock encryption is used for encryption in the D-Collection phase to achieve data confidentiality. A fog node checks the identification of sensing data in two crowdsensing reports by comparing the ciphertexts. Consistency is checked after decryption to prevent duplicate-faking attacks. To record the contribution of the mobile users with deduplicated data, key homomorphic signature is utilized to sign the sensing data, followed by aggregation of signatures of mobile users by fog nodes. The proxy re-encryption scheme is applied to prevent spatial task data from being disclosed to external attackers. The scheme also enables the SC-server to re-encrypt for generating the ciphertexts that fog nodes can decrypt. Some researchers used blockchains [56] to improve the security of decentralized or fog-based crowdsourcing. To prevent malicious users' participation, user privacy leakage, and so on, [PERSON] [57] proposed a fog-blockchain architecture for crowdsourcing reputation management. Figure 19 shows the proposed framework. There are four elements: crowdsourcing platform, the task requesters, fog-blockchain layer, and mobile workers layer. A cross-layer privacy protection scheme was designed to separate the tasks and user's identity and tasks. There are three phases: system initialization, crowdsourcing services, and reputation management. Each user is registered on the crowdsourcing platform and provides their information in the system initialization phase. In the second phase crowdsourcing service phase, a task is submitted by the task requestor to the platform, which assigns the encrypted task to the fog nodes which match the task requirements. In the last phase of reputation management, the platform then decrypts, analyzes, and evaluates the task results. The Workers' reputation values were then calculated, and the reputation records were feedback to the fog nodes. At last, the workers' reputation with the real user ID was updated by the fog nodes update in the maintained blockchain. Theoretical security analysis was evaluated, and experiments were conducted to evaluate the proposed CrowdR-FBC framework.