# Towards Crowdsourcing Internet of Things (Crowd-IoT): Architectures, Security and Applications
## 4 Trustworthy, Security and Privacy in Crowdsourcing Internet of Things
### Privacy in Crowd-IoT  
For specific works of privacy in IoT, privacy maintaining framework for IoT was developed by [PERSON] [39]. In this work, the authors focused on data controllers, data processors, and integrators for privacy preservation. The authors in [40] proposed a privacy-preserving IoT architecture to maintain privacy in large-scale IoT networks. The authors in [41] proposed an approach termed Lightweight Privacy-preserving Data Aggregation (LPDA) for Fog computing-enhanced IoT. The proposed LPDA combines homomorphic encryption, Chinese Remainder Theorem, and hash chaining to enable fog device aggregation at the network edge. Although there are many works on privacy issues in crowdsourcing, there are few works on privacy preservation in specialized crowdsourcing IoT. This subsection reviews recent and specific research on centric crowdsourcing IoT and edge-based crowdsourcing IoT. [PERSON] [42] proposed a crowdsourcing privacy framework under fog computing architecture. Figure 8 shows the framework, which has four components: (1) Requester who collects data and sends the tasks to the crowdsourcing server, (2) Server to assign tasks to participants, provide incentive mechanism and manage the data collection, (3) Fog nodesfor tasks assignment and data collection in the selected region, and (4) Workers who select the task (fog node) and report their location information. The authors proposed a novel approach to protect participants' location information. The key idea is to utilize the fog nodes to collect statistics of different paths. The proposed architecture contains three main components (iterative statistics, adaptive sampling, and secure reporting). Theoretical analysis and experiments were performed to show that the approach could provide a strict privacy guarantee and enhance the performance. [PERSON] [43] aimed to address the issue of privacy-preserving crowdsourcing. They proposed an approach to managing the workers' computational requirements by allocating parts of the computation to the fog node. The proposed fog-assisted crowdsourcing system for task allocation is shown in Figure 9. There are five key components in this system: (1) Crowdsourcing server; (2) Workers; (3) Customers; (4) Fog node; and (5) Key generation center (KGC). The first four components are general in any crowdsourcing IoT or fog-based crowdsourcing. The KGC is a trusted entity responsible for initializing the system parameters (e.g., public key, master secret key) and allocating secret keys to customers and workers. The fog nodes are deployed at the network edge. These nodes share the partial computation from the end-users or works. The privacy-preserving task allocation scheme performed the privacy-preserving task-and-keywords matching. The workers who satisfy the task requirements can decrypt the cipher-text of task content and can be appointed. The workers produce the cipher-text of interest keywords and send the data to the crowdsourcing server. Their approach achieves privacy protection of task content and interest keywords and resists the attacks of the workers. Spatial crowdsourcing is a specific type of crowdsourcing and has drawn much attention in mining. It allows multiple task owners or requestors to outsource their tasks to the central server for recruiting participants. The task release and data aggregation may have privacy threats for both parties. The central server does not fully know the entire geographical user distribution. [PERSON] [44] proposed to address the above problem by presenting a fog-assisted spatial crowdsourcing architecture. Figure 10 shows their fog-assisted spatial crowdsourcing system.