You decide how you want to anonymise your data
Data Privacy Guard provides a wide variety of different anonymization algorithms, all configurable on a per-column bases, many of which provide additional options to customize their behaviour
Replace changes all row values for the configured column to a pre-defined value. This method is an example of a generalisation technique in which all values are identical thus making identification based on unique characteristics impossible.
Configuring the Remove method on a columns removes, or empties, all values inside that column.
This can be very useful in situation where rows of the column you are configuring it on don’t always have values, or the data inside the column isn’t used in the processes after anonymisation.
The Randomize method is based on our in-house developed algorithm that is able to replace a row value with a randomly selected different value that exists inside the column. Because of the random nature of the algorithm results are unpredictable and are different each time the anonymisation method is executed.
Randomize can also be configured with two additional options: Keep Unique and Keep Distribution.
Keep Unique performs an additional check to make sure the value selected for anonymisation is not identical to the original value, if it is, the algorithm selects a different value.
Keep Distribution modifies the algorithm in a way that the distribution of data before anonymisation is identical to after anonymisation. Simply put, if you randomize a “first name” column and it contains four occurrences of the name “Paul”, after anonymisation of the column with Keep Distribution enabled, the name “Paul” is returned exactly four times.
Randomize Collection is an extension of the Randomize method but instead of configuring it on a single column it can be configured on a collection of columns (up to a maximum of three). All the columns inside the collection stay together during the anonymisation process, meaning the relation between the columns stays intact. This method can, for example, be used to keep geographical data together, or keep the postal code column together with the city column to avoid ending up with a postal code of a different city after anonymisation.
With the Randomize Pool method all row values for the configured column are replaced by a randomly selected value from a collection of values you configure. This method is useful in situation where, for instance, you have a number of values that are useable after anonymisation, and you want the original column values to be one of those values when the anonymisation process is completed.
The Randomize Interval method replaces a column value with a new value based on an interval you define. For instance, if you configure the Randomize Interval method on a column that contains dates, you can define an interval of 10 days. When anonymising the data a random data will be selected between -10 and +10 days of the dates stored in the column.
Randomize Interval can be configured on numerical columns and date columns. The latter can be configured on a scale of days, months and years while the former has no effective limitation in the interval range.
With the Hash method you can change all the values inside a column to a random combination of letters and numbers. The value that is returned after executing the Hash method is not based on the original value but instead is a pure random value.
The Truncate method does exactly what it is named after, removing all the contents of a table. This method is only available when anonymising database targets like Oracle or Microsoft SQL Server, and is frequently used to quickly empty tables that, for instance, contain logging information that is not required post-anonymisation.
All of our anonymisation methods are optimised for speed and accuracy, making sure data cannot be used for identification
Each existing and new method is automatically tested millions of times to make sure they are secure, fast and accurate
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