LAUSR

Spatial crowdsourcing is a promising architecture that collects various types of data online with the help of participants powerful mobile devices. Humans are involved in the crowdsourcing process, thereby increasing its accuracy; however, it is also associated with some privacy and security problems. The crowd tasks are executed in participants mobile devices, and the results are send to the server through networks, so that attackers could eavesdrop participants location information. Thus, we studied and proposed a spatial task assignment method for privacy-aware spatial crowdsourcing using a secure grid-based index. The secure grid index used an encrypted grid number and grid cell-based local coordinate system to protect participants location privacy. By using the grid based index in spatial task management process, it also could increase the spatial task processing time. In the experimental test, we showed that the proposed method is faster than the current method and extremely efficient when the spatial crowdsourcing tasks are geometry based tasks.