Machine Learning and Data Mining imply notable privacy vulnerabilities since they have the potential to expose confidential details about people or collectives that have contributed to the data. This paper proposes a Generalised Privacy Assessment Methodology that can be universally applied to any Machine Learning model and its corresponding Data Source. The suggested technique facilitates the quantification of the Vulnerability Score for any given data source or model. We demonstrate the practicality and efficacy of the concept by implementing it in several scenarios, ranging from a small-scale IoT project to a hypothetical extensive database, like those used by governmental entities.