Abstract
An outsourcing service named spatial crowdsourcing (SC) becomes popular, whereby the SC-server allocates nearby tasks to the workers based on the outsourced task and worker locations. Exposing real locations can cause serious privacy leakage. However, traditional differential privacy (DP) and encryption methods do not consider the dynamic worker location and correlation privacy. Here, a Local DP-based dynamic worker location protection (LDPDW) scheme is proposed to achieve high-quality task allocation and locally protect the correlation and location privacy of dynamic workers. Specifically, LDPDW generates noisy high correlated graph classes and obfuscates the worker locations in a static case by adopting a LDP-based correlation graph (LDPCG) algorithm and distance score-based LDP (DSLDP) algorithm, thereby achieving controlled noise addition and ensuring the correlation and location privacy. To support the privacy-preserving dynamic locations, a dynamic correlation graph-based location obfuscation (DCGLO) algorithm is proposed to allocate reasonable privacy budget $\epsilon $ , which ensures the data utility. Finally, a linear acceptance model-based task allocation (LAMTA) algorithm is used to allocate tasks to the workers with high acceptance rates. Privacy analysis and the extensive experimental results show that our LDPDW scheme follows $\epsilon $ -LDP while allocating tasks with high data utility.
Highlights
A new Spatial Crowdsourcing (SC) platform has become popular promoted by the rapid development of mobile devices, whereby the SC-server allocates the tasks outsourced by the requesters to appropriate workers based on the task and worker locations [1]–[3]
We explore using Local Differential Privacy (LDP) to solve the problems of both local location privacy and correlation privacy of dynamic workers in SC, in which the SC-server performs high-quality task matching from the noisy dynamic locations outsourced by the workers themselves
Since we are the first to consider the privacy-preserving dynamic workers in SC, based on different parameters, we only evaluate the performance of our scheme without considering comparing the state-of-the-art static LDP methods
Summary
A new Spatial Crowdsourcing (SC) platform has become popular promoted by the rapid development of mobile devices, whereby the SC-server allocates the tasks outsourced by the requesters to appropriate workers based on the task and worker locations [1]–[3]. We explore using LDP to solve the problems of both local location privacy and correlation privacy of dynamic workers in SC, in which the SC-server performs high-quality task matching from the noisy dynamic locations outsourced by the workers themselves To achieve this goal, we need to face the following three challenges: (i) since the workers’ locations are correlated, which can leak the location privacy [43], [45]. To protect the worker location and correlation privacy locally, the workers use LDP method to obfuscate the high correlations and dynamic locations, respectively. LDP-BASED DYNAMIC WORKER LOCATION PROTECTION SCHEME we propose a LDP-based dynamic worker location protection (LDPDW) scheme to achieve the locally privacy-preserving dynamic worker locations and correlations while allocating high-quality tasks. TaskAlloc Module: At any time, upon receiving the task location LT and all the workers’ obfuscated location information {(L(W1), RW1 ), (L(W2), RW2 ), · · · , (L(Wn), RWn )}, the SC-server first searches the workers close to the task location by calculating the distances between the obfuscated locations and the real locations, adopts a Linear acceptance model to obtain the allocated workers {Wz1 , Wz2 , · · · , Wzk }, and notifies these corresponding workers by returning the allocated workers and the task location information ({Wz1 , Wz2 , · · · , Wzk }, LT )
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