Description: Privacy metrics are used to assess the level of privacy protection in data handling processes. These metrics are fundamental to ensuring that individuals’ personal data is not exposed or misused. In a world where data collection and analysis have become ubiquitous, privacy metrics allow organizations to measure and manage the risks associated with handling sensitive information. There are various metrics, such as differential privacy, which provides mathematical guarantees about data privacy, and k-anonymity, which aims to ensure that an individual’s information cannot be distinguished from at least k-1 individuals in a dataset. These metrics not only help companies comply with privacy regulations, such as GDPR in Europe, but also foster consumer trust by demonstrating a commitment to data protection. In the context of data handling, where patterns and trends are extracted from large volumes of information, the implementation of privacy metrics becomes crucial to balance the utility of data with the need to protect individual privacy.
