Datenunterdrückung

Datenunterdrückung, also known as data suppression or data masking, is a technique used to obscure or remove sensitive information from datasets. This is typically done to protect privacy or to comply with data protection regulations. The goal is to make the data unusable for identifying individuals or revealing confidential details while still retaining its analytical value.

There are several methods of data suppression. One common approach is to replace sensitive values with generic placeholders, such as "X" or null values. Another method involves aggregating data, where individual records are grouped into larger categories, making it impossible to pinpoint specific entries. Generalization is also employed, where precise values are replaced with broader ranges, for instance, replacing an exact age with an age bracket. Anonymization, a related but distinct process, aims to remove all personal identifiers from a dataset.

Data suppression is crucial in various fields, including healthcare, finance, and market research, where the handling of personal information is paramount. It enables organizations to share data for research, analytics, or testing purposes without compromising the privacy of individuals. By carefully applying suppression techniques, organizations can balance the need for data utilization with the ethical and legal obligations to protect sensitive information. The effectiveness of data suppression relies on choosing appropriate methods based on the type of data and the intended use of the anonymized dataset.