Privacy Preserving Fog-Enabled Dynamic Data Aggregation in Mobile Phone Sensing

With the development of science and technology, mobile phones have gained unprecedented popularity, subsequently the applications based on mobile phone perception have become widespread. Mobile sensing encourages many users to participate in data collection tasks through their mobile phones, but this process raises privacy issues. Previous studies have either used additive homomorphism to protect data privacy or aggregated methods to provide identity privacy protection. However, little research has been done on data dynamics and how to improve aggregation efficiency in multi-user scenarios. To solve this problem, we propose a privacy preserving fog-enabled dynamic data aggregation protocol in mobile phone sensing, named FDDA. In the proposal, we first add fog nodes to our framework, allowing fog nodes to aggregate a set of user data at the same geographical location without identifying the data sources. Then, we design data dynamics strategy to support the user joining and revoking effectively. Finally, we show that our protocol can be well applied in actual scenarios through security analysis and simulation experiments.