Privacy-aware task data management using TPR*-Tree for trajectory-based crowdsourcing

Abstract

Spatial crowdsourcing is a promising architecture for collecting various types of data online with the aid of participants’ powerful mobile devices. However, it is also associated with certain privacy and security issues, which can reduce the quality of the crowdsourcing service. Some crowd tasks require the collection of connected data points. When a location-anonymous method is employed to ensure the privacy of location data points, the location trajectory data may become meaningless. To solve the privacy problem for trajectory data in large-scale crowdsourcing systems, we proposed a spatial task management method for privacy-preserving trajectory-based crowdsourcing, using a 3DES encryption and compressive-sensing-based trajectory data decryption method which is called DES-TraVec (3DES-based trajectory vector) cryptography algorithm. To provide a real-time crowdsourcing service, we proposed the use of an extended TPR*-Tree to bulk load the crowdsourcing results and manage the benders service requests so that the proposed method could support participants’ privacy and ensure quick answers for crowdsourcing services. The experimental results demonstrated that the proposed method is efficient in preserving trajectory-based crowdsourcing data and is faster than the current method.