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A GUIDE TO BASIC ANONYMISATION
Applicable to:
STEP 3
APPLY ANONYMISATION TECHNIQUES
Internal data sharing (de-identified data) Long-term data retention
Internal data sharing (anonymised data) or External data sharing Synthetic data
In this step, you will apply anonymisation techniques to the indirect identifiers so that they cannot be easily combined with other datasets that may contain additional information to re-identify individuals. For the synthetic data use case, anonymisation techniques should also be applied to the target attributes. Do note that application of these techniques will modify the data values and may affect utility of the anonymised data for some use cases (e.g. data analytics). The anonymisation techniques recommended below take into consideration potential utility required for record-level data in each use case. Organisations may use other anonymisation techniques beyond what is recommended, if relevant to their use case. Use case
Suggested anonymisation techniques for record-level data
Internal data sharing (anonymised data)
• Record suppression: The removal of a record (i.e. row of data, especially where such data may contain unique data values that cannot be anonymised further).
or
• Attribute suppression: The removal of a data attribute (i.e. column of data, especially where such data is not needed in the dataset and may contain unique data values that cannot be anonymised further).
External data sharing
• Character masking: The replacement of some characters of the data value with a consistent symbol (e.g. * or x). For example, masking a postal code would involve changing it from “235546” to “23xxxx”. • Generalisation: The reduction in granularity of the data (e.g. by converting a person’s age into an age range). For example, generalising the age of a person from “26 years old” to “25-29 years old”.
