# 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
The architecture contains the following components: (1) Spatial crowdsourcing server which provides aggregation services by assigning corresponding tasks of requestors or owners to fog nodes, (2) Tasks owners who generate spatial aggregation tasks and collects the data from assigned locations, (3) Fog nodes which are deployed at the network edge to manage the task distributions in a geographical area, (4) Mobile participants who carry the mobile sensing devices and perform the sensing tasks, and (5) Authority center which has the responsibility for managing the system including registration of system entities, and key distribution. Figure 8: Crowdsourcing privacy framework under fog computing architecture [42]. In their architecture termed as PTAA (Privacy Aware Task Allocation and Data Aggregation), fog nodes are deployed in various locations to distribute the tasks and perform the data aggregation in a privacy-aware manner. The PTAA approaches consists of several phases: (1) System initialization; (2) Task generation/allocation; (3) Data collection/aggregation; (4) Data decryption; and (5) Data update. In their proposed scheme, bilinear pairing and homomorphic encryption were applied. There are two steps of task allocation in PTAA. This allows the server to allocate the task to fog nodes without requiring knowledge of the task content. To protect the user or participant data privacy, sensing data are encrypted before being sent to the fog nodes. Fog nodes can also detect invalid data, such as modified data from adversaries. In the aggregation phase, secure aggregation protocols enable the fog nodes and the server to compute the encrypted aggregated result. The authors in [45] proposed a privacy-preserving incentive mechanism involving multi-hop routing to preserve task owners or requester's privacy for social IoT crowdsourcing. Figure 11 shows the privacy-preserving incentive mechanism for the social crowdsourcing service. The authors incorporated privacy and budget feasibility constraints into a problem formulation of utility maximization. The objective of the mechanism is to improve the overall service performance by dividing it into subtasks and routing it to workers. Guidelines on the subtasks division from an entire task and the tradeoff between task accuracy and privacy are provided. In their proposed work for social IoT, people with the direct connection to task requester are considered as 1-hop friends, while 2-hop friends are  
Figure 10: PTAA fog-assisted spatial crowdsourcing architecture [44]. Figure 9: Fog-assisted crowdsourcing system for task allocation [43]. those who do not have a direct connection to a requester but have a direct connection with 1-hop friends. There are two stages in their multi-hop routing incentive policy: (1) One-hop myopic routing; and (2) Multi-hop payment allocation, to have an equilibrium to motivate workers to forward subtasks to friends with the appropriate expertise. They showed that the cost for the task requester would increase with the length of the task forwarding chain in multi-hop routing. Simulation results verified the theoretical analysis, and the proposed work could achieve high information gain with a privacy guarantee.