Privacy-preserving data aggregation in Participatory Sensing Networks

Participatory sensing using mobile devices is emerging as a promising method for large-scale data sampling. A critical challenge for participatory sensing is how to preserve the privacy of individual contributors' data. In addition, the integrity of the data aggregation is vital to ensure the acceptance of the participating sensing model by the participants. Existing approaches to these issues suffer from excessive communication cost, long delays or rely on a trusted third party. The objective of our research is to design a data-aggregation scheme for participatory sensing systems that addresses user privacy and data integrity while keeping communication overhead as low as possible. We propose four techniques to address these challenges and validate them through analytical models and simulations.