Nowadays we have an unprecedented opportunities of sensing, storing and analysing data describing human activities at extreme detail and resolution.
Unfortunately, personal data are sensitive, because they may allow re-identification of individuals in a de-identified database.
The paradoxical situation we are facing today is that we are fully running the risks without fully catching the opportunities of big data: on the one hand, we feel that our private space is vanishing in the digital, online world, and that our personal data can be used without feedback and control; on the other hand, the same data are seized in the databases of companies, which use legal constraints on privacy as a reason for not sharing it with science and society at large, keeping this precious source of knowledge locked to the data analyst.
This project aims at applying a privacy-aware framework for sharing data to a chosen scenario.
The framework enables the assessing both the empirical privacy risk associated to the individuals represented in the data, and the data quality guaranteed only with users not at risk.