Privacy, quality of information, and energy consumption in Participatory Sensing systems

Participatory Sensing (PS) is a new data collection paradigm based on the voluntary participation of many cellular users equipped with smart applications, a large diversity of sensors, and Internet connectivity at all times. Although many PS-based applications can be foreseen to solve interesting and useful problems, many of them have not been fully implemented and used in practice because of privacy concerns. Compounding the problem, privacy-preserving mechanisms introduce additional issues. For example, one of the most important problems is that of the quality of the information provided by the PS system to the final users. The problem is that, in order to protect the privacy of the users, most privacy-preserving mechanisms modify their real locations, which makes the reported data as if it had been measured from a different location, introducing noise or false information in the system and to the final users. Another important problem is that of the energy consumption. Privacy-preserving mechanisms consume extra energy and users are not very willing to use PS applications if they drain their batteries considerably faster. This paper proposes a hybrid privacy-preserving mechanism that combines anonymization, data obfuscation, and encryption techniques to increase the quality of information and privacy protection without increasing the energy consumption in a significant manner. A new algorithm is proposed that dynamically changes the cell sizes of the grid of the area of interest according to the variability of the variable of interest being measured and chooses different privacy-preserving mechanisms depending on the size of the cell. In small cells, where users can be identified easier, the algorithm uses encryption techniques to protect the privacy of the users and increase the quality of the information, as the reported location is the real location. On the other hand, anonymization and data obfuscation techniques are used in bigger cells where the variabilit- of the variable of interest is low and therefore it is more important to protect the real location (privacy) of the user. We evaluated our hybrid approach and other privacy-preserving mechanisms using a real PS system for air pollution monitoring. Our experiments show the better performance of the proposed hybrid mechanism and the existing trade-offs in terms of privacy, quality of information to the final user, and energy consumption.