The current state of the art of privacy-preserving techniques in participatory sensing. The first implementation of a privacy aware architecture, named Anony Sense, for the anonymous task allocation and data reporting. From the perspective of cryptography, analyzed the realistic architectural assumptions and privacy requirements, and then provided an instantiation that achieved privacy protection in participatory sensing with provable security. Surveyed the privacy and security implications in three types of application scenarios. They analyzed the privacy challenges in participatory sensing applications in detail. Surveyed the privacy protection in terms of data privacy protection, location privacy protection and trajectory privacy protection in location-based services. Reviewed the definitions, the models and the appropriate location privacy protection techniques from the perspective of mobile data management. the development of wireless communication technologies, such asWLAN, 3G/LTE,WiMax, Bluetooth, Zigbee, and so on, mobile devices are equippedwith a variety of embedded sensors surveyed in as well as powerful sensing, storage and processing capabilities. Participatory sensing (urban sensing , which is the process that enables individuals to collect, analyze and share local knowledge with their own mobile devices, emerges as required under these well conditions. Compared with WSNs, participatory sensing offers a number of advantages on deployment costs, availability, spatial- temporal coverage, energy consumption and so forth. It has attracted many researchers in different areas such as Intelligent Transportation System, healthcare and so on. There are lots of existing prototype systems that include CarlTel, BikeNet, DietSense, PEIR and so on. Nowadays, participatory sensing applications mainly depend on the collection of data across wide geographic areas. The sensor data uploaded by participators are invariably tagged with the spatial-temporal information when the readings were recorded. According to the analysis in the possible threats to a participator’s privacy information that include monitoring data collection locations, tracing his/her trajectory, taking photographs of private scenes and recording the intimate chat logs.