Hierarchical & Global Recoding
Transforms data into broader categories using a predefined hierarchy (e.g., replacing exact ages with age groups like “20-30”). This reduces data granularity, enhancing privacy while retaining meaningful information.
Transforms data into broader categories using a predefined hierarchy (e.g., replacing exact ages with age groups like “20-30”). This reduces data granularity, enhancing privacy while retaining meaningful information.
Groups numeric values into bins (e.g., income ranges) or aggregates data points to create summarized categories, limiting the precision of individual records and reducing re-identification risks.
Clusters similar data points and replaces them with their average (micro-aggregation) or approximates values to the nearest round number. This minimizes individual-level details while preserving overall data trends.
Caps extreme values at predefined limits (e.g., incomes above a certain threshold are recorded as “Above $200K”), protecting privacy for outliers while maintaining the dataset’s usability for analysis.