IoT-Privacy: To be private or not to be private

Privacy breaching attacks pose considerable challenges in the development and deployment of Internet of Things (IoT) applications. Though privacy preserving data mining (PPDM) minimizes sensitive data disclosure probability, sensitive content analysis, privacy measurement and user´s privacy awareness issues are yet to be addressed. In this paper, we propose a privacy management scheme that enables the user to estimate the risk of sharing private data like smart meter data. Our focus is to develop robust sensitivity detection, analysis and privacy content quantification scheme from statistical disclosure control aspect and information theoretic model. We depict performance results using real sensor data.