Although data masking and de-identification are often grouped together for discussion, the two use different approaches in making data anonymous. Furthermore, masking and de-identification deal with different identifiers in a dataset. Masking is used to anonymize direct identifiers while de-identification is used to anonymize quasi-identifiers. In practice, masking and de-identification should be used together to optimize the balance between protecting privacy and maintaining the usefulness of the data. This paper explores the major limitations of using data masking on its own, without de-identification.
In this white paper, learn exactly what the top 5 drawbacks are to using only data masking and why they need to be avoided.
