Local Privacy-Preserving Dynamic Worker Locations in Spatial Crowdsourcing

An outsourcing service named spatial crowdsourcing (SC) becomes popular, whereby the SC-server allocates nearby tasks to workers based on outsourced task and worker locations. Exposing real locations can cause serious privacy leakage. However, traditional differential (DP) encryption methods do not consider dynamic location correlation privacy. Here, a Local DP-based protection (LDPDW) scheme is proposed achieve high-quality allocation locally protect of workers. Specifically, LDPDW generates noisy high correlated graph classes obfuscates in static case by adopting LDP-based (LDPCG) algorithm distance score-based LDP (DSLDP) algorithm, thereby achieving controlled noise addition ensuring To support privacy-preserving locations, graph-based obfuscation (DCGLO) allocate reasonable budget $\epsilon $ , which ensures data utility. Finally, linear acceptance model-based (LAMTA) used with rates. Privacy analysis extensive experimental results show that our follows -LDP while allocating