Home – Data Privacy Guard – Anonymization methods
You decide how your data is anonymized
Data Privacy Guard provides a wide variety of anonymization algorithms. Check out the following tabs to see what the possibilities are for your dataset.
Replace changes all row values of the configured column to a predefined value. This method is an example of a generalization technique by which identification based on personal characteristics is made impossible.
Using the Remove method on a column clears all values inside that specific column. This is the simplest method that can be used on columns that have no purpose when the anonymization process has completed.
The Randomize method replaces all values with a randomly selected value that pre-existed in the column. The random nature of the algorithm assures unpredictable and different values each and every time the anonymization process is executed.
Randomize Collection is an extension of the Randomize method. Using this method, the algorithm is not executed on a single column but rather on a set of up to three columns. This way, all row values of these columns keep their connection during the anonymization process preserving the relationship between these columns for every row.
The Randomize Pool method replaces all values within a column by a randomly selected value from a set of values specified by you. This option comes in handy when, for example, only a few specific values are useful in your testing processes.
Randomize Interval replaces the numerical or date values in a column with a value based on an interval you define. For example, if you configure Randomize Interval on a date column you can set the interval to 10 days. When the column is anonymized, all rows within the column will receive a randomly selected date that is between -10 and +10 days removed from the original date.
Tokenize is a pseudonymization algorithm that replaces all row values of a column by a randomly generated key value. By applying this method, it is possible to maintain relationships between multiple datasets. Another reason to apply this method is when, for example, your research requires the row value of the original dataset to be retrieved.
Using the Hash method, all values in a column are replaced by a random combination of letters and numbers.
Truncate does exactly as its name suggests, it completely removes the entire content of the table. You can only apply this method when you are anonymizing a table within a database.
Do you have more detailed wishes to anonymize your data? Customize your anonymization process even further with these additional options!