Data Anonymization is a legacy method of protecting data privacy through information sanitization which involves encryption, destruction, or suppression of data to erase personally identifiable indicators that connect to individuals from the stored datasets.
By encrypting or suppressing data, privacy teams essentially ensure that data cannot be viewed by anyone including the data teams that require access to unrestricted data sets, especially for advanced machine learning models. This makes anonymized data useless in most cases. Therefore for organizations, it comes down to either preserving data privacy or data utility. A choice with no good answer. This is why often data teams opt to work with highly sensitive real data, risking legal action if data is compromised.
But this isn’t all. Data anonymization is not as effective in protecting data privacy as you might think. 87% of the population could be reidentified using their gender, ZIP code, and date of birth by connecting them even though they are non-identifiable separately. While re-identifying anonymized data is not an easy task it is not impossible. This makes anonymized data not only not usable by data scientists but also endangers data privacy.
Synthetic Data is a subset of GenAI that looks, feels, and functions like real data. It is generated by running real data through advanced ML models that generate a 99% identical replica of the highly sensitive production data. The synthetic data created have nearly identical statistical properties and structure as the real data, and since it does not create any real consumer data it cannot be traced back to any individual making it privacy proof. Therefore it is not bound by any data privacy and protection laws, making it easier and safer for organizations to access, use, and share data.
Furthermore, synthetic data is not only applicable to privacy protection. Other benefits of synthetic data include bias mitigation in datasets, and generating additional data for rare scenarios using limited real data.
In summary, data anonymization techniques, which remove information to protect privacy, have been established to be ineffective in completely protecting data privacy since anonymized data can be re-identified, and due to suppression, data utility is decreased. While synthetic data is data that does not directly link to real users, is 100% secure. In addition, synthetic data can be used to train more robust ML models through enhancement, bias mitigation, and rebalancing data.