# Personalized Privacy Protection Based on Space Grid in Mobile Crowdsensing
## 4 Personalized Privacy Protection
### Task Broadcast Strategy Design  
Compared to tasks assigned directly by the platform, workers are more willing to accept the broadcast task, so the platform needs to find a reasonable broadcast area. In essence, the task broadcasting strategy must access the spatial indexes of workers and calculate a designated broadcast grid area centered around the task location. The decisive condition of this area is that the task can be accepted by the staff in the platform when the probability of completion can be achieved. As users generally prefer tasks closer to \begin{table}
\begin{tabular}{c c} \hline \hline
**Symbol** & **Definition** \\ \hline \(queue\) & auxiliary queue \\ \hline \(curr\) & current team head node \\ \hline \(d_{curr}\) & depth of node \(curr\) in PAG tree \\ \hline \(subWL1_{curr1d}\) & subgrid corresponding to node curr \\ \hline \(\varepsilon_{1,node_{i}}\) & personalized privacy budget of the first-level grid \\  & node \(i\) \\ \hline \(\varepsilon_{2,node_{i}}\) & remaining privacy budget \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Symbols required for Algorithm 1. Figure 3: The flowchart of Algorithm 1.  
their current location for convenience [38; 39], their acceptance rates vary depending on task proximity. The acceptance rate is a function that decreases with forward distance. In this paper, the linear decline function is selected. This chapter adopts the broadcast area algorithm described in Reference [35]. This paper finds the secondary grid node where the task is located and continuously adds the neighbor nodes with workers. When there is a certain probability that the task can be completed, it ends the expansion of the broadcast area. ```
0: Grid of workers' location (WL)
1: calculate \(m_{1}\)
2: Initialize the root node, initialize the queue and queue the root node
3: if queue is empty:
4: output the PAG tree
5:else:
6: pop queue leader node curr
7: If \(d_{curr}\) = 0:
8: divide WL into \(subWL1_{1}\), \(subWL1_{2}\),..., \(subWL1_{m1}\)
9: establish first-layer nodes of PAG tree
10: put first-layer nodes into queue
11: randomly generated budget percentage \(\alpha\)
12: calculate \(\varepsilon_{1,node_{i}}\) and remaining privacy budget \(\varepsilon_{2,node_{i}}\)
13: execute step 3
14: else:
15: If \(d_{curr}\) = 1:
16: divide \(subWL1_{currId}\) into \(subWL2_{1}\), \(subWL2_{2}\),..., \(subWL2_{m1}\)
17: establish second-layer nodes of PAG tree
18: add second-layer nodes to Laplace random noise count
19: put second-layer nodes into queue
20: execute step 3
21: else:
22: If \(d_{curr}\) = 2:
23: calculate \(m_{1}\)
24: divide \(subWL2_{currId}\) into \(subWL3_{1}\), \(subWL3_{2}\),..., \(subWL3_{m2}\)
25: establish third-layer nodes of PAG tree
26: add third-layer nodes to Laplace random noise count
27: put third-layer nodes into queue
28: execute step 3
29: else:
30: divide \(subWL3_{currId}\) into \(subWL4_{1}\), \(subWL4_{2}\),..., \(subWL4_{m2}\)
31: establish fourth-layer nodes of PAG tree
32: add fourth-layer nodes to Laplace random noise count
33: put fourth-layer nodes into queue
34: execute step 3
```  
**Algorithm 1** PAG  
According to the literature [39], approximately 10% of the platform's workforce is responsible for more than 80% of the tasks; these individuals are referred to as super workers. Moreover, within the group of super workers, around 90% of them travel less than 40 miles a day. This attribute has been labeled as task locality in the literature [37]. Additionally, prior research [40] addressed the issue of content locality among users of platforms such as Flickr and Wikipedia, proposing the spatial content generation model (SCPM). The model calculates the average contribution distance (\(MCD\)) of each worker as follows:  
\[MCD(w_{i})=\sum_{i=1}^{n}\frac{d(w_{i},t_{j})}{n} \tag{9}\]  
where \(d(w_{i},t_{j})\) represents the distance between worker \(w_{i}\) and task \(t_{j}\).