Privacy-Preserving Data Aggregation over Incomplete Data for Crowdsensing

Crowdsensing recently attracts great attention from both industry and academia. By fusing and analyzing multi- dimensional sensing data collected from crowdsensing users, it is possible to support health caring, environment mentoring, traffic mentoring and social behavior mentoring. Nonetheless, how to preserve users' data privacy during data fusing, e.g., data aggregation, has been rarely discussed for crowdsensing before. Besides, due to the dynamics of sensing environments and available resources at users, there will be missing elements from users' sensing results. In this paper we aim to achieve privacy-preserving data aggregation over incomplete data for crowdsensing. A novel scheme is developed based on linear transformation and homomorphic encryption scheme. It enables the server to obtain aggregation results over recovered sensing results without learning their individual details. Security analysis and performance evaluation are conducted showing the effectiveness and efficiency of our scheme.