A Privacy-Preserving Identification Mechanism for Mobile Sensing Systems

In the applications for mobile sensing, the trustworthy of sensed data should be put on the first place. The identification of participants can ensure data trustworthy but will reveal the privacy of the participants to a great extent. In this paper, we propose a privacy-preserving identification mechanism for mobile sensing systems to select sensed data dynamically to protect participant's sensitive information. It solves the contradiction between "privacy protection"and "identification". It divides data privacy sensitivity of the data sensed from the task that participants attended, allowing participants to define their own privacy sensitivity, then selects sensed data dynamically and uses differential privacy to process the data with high privacy sensitivity. It can not only protect participants' privacy, but also identify participants' IDs. In order to achieve identification, a two-layer neural network model is used to train and learn the participant's style of action and generate an identity feature database. The experimental results show that the proposed mechanism can provide a trustworthy platform for mobile sensing systems.