In differential privacy, privacy is conceptualised as a finite resource that is consumed as data is analysed. This means that each query or analysis of the data uses a portion of the available privacy budget. The key is to manage and quantify this consumption of privacy, typically represented by the epsilon and delta parameters. As more queries are made or more data is used, the privacy budget diminishes, indicating an increase in the potential for privacy loss. This conceptual framework allows for a systematic and quantifiable approach to managing privacy risks, ensuring that data analysis does not excessively compromise individual privacy.