Secure Mobile Crowdsensing Obfuscation for Patient Feedback Using Location Privacy Approach

Abstract

The broad of smart gadgets prompts the improvement of progressively complex distributed applications, attractive endeavours from both research and business networks. Recently, a new volunteer commitment worldview dependent on participatory and sharp detecting is equipping in the Internet of Things scenario: Mobile Crowdsensing, Sparse Mobile Crowdsensing (MCS) has become a compelling approach to urban- scale sensing information acquiring and making inferences. However, while recording data with their real sensing locations, participants threaten their location confidentiality. To address this issue, Sparse MCS embraces differential- security to give a hypothetical assurance to the area protection of patients or individuals regardless of prior knowledge of an adversary. It was designed to provide aggregated data to patients about their health variation over time. A mobile feedback form platform will help a patient to understand and get better control of their health. In addition, to decrease the quality of data induced by differential location obfuscation, a security protecting framework is proposed.