Data anonymization - synthetic data for maximum privacy and utility

• Data anonymization is a task that is increasingly challenging to meet. Anonymizing data successfully is practically impossible for complex datasets. As few as 15 characteristics are enough to reidentify people in the US. 
• Protecting this data from leaking is also impossible if companies rely only on protecting it from attacks from the outside when 59% of privacy incidents originate with an organization's own employees. 
• According to a financial data risk report, new hires at financial institutions - organizations with the highest level of data restriction - have unrestricted access to 11 million files on their first day of work.
• Old data anonymization tools, like randomization, permutation, and generalization, destroy feature correlations and render the data useless for training machine learning models.
• Pseudonymization or data masking is not data anonymization either from a legal or practical standpoint. Pseudonymized data is still personal data and is very vulnerable to privacy attacks.

Legacy data anonymization tools are still widely used by organizations. These old-school data anonymization techniques, like aggregation, generalization, permutation, hashing, or randomization, endanger privacy and destroy data utility. For advanced data use cases, like machine learning development, these techniques are useless. As a result, data scientists and machine learning engineers work with highly sensitive production data, regardless of the risks involved.

Synthetic versions of datasets offer a privacy-safe, 100% GDPR-compliant drop-in placement for downstream tasks. The process of synthesization retains statistical properties, correlations, and referential integrity across datasets. The preserved utility makes synthetic data a perfect choice for intelligence-hungry use cases, like machine learning development, and easy but critical tasks, like data democratization, in compliance with even the strictest data protection laws. According to the EU's Joint Research Center, "synthetic data changes everything from privacy to governance."

Synthetic data is increasingly seen as the most robust privacy-enhancing technology ready for widespread adoption. We first saw large enterprises handling sensitive customer data, like banks and insurance companies, leading the way with the adoption of synthetic data technologies. With the emergence of new use cases, like test data generation and machine learning development, smaller companies and individual developers started using privacy-safe synthetic data in their everyday work. For best results, it's important to monitor synthetic data quality for privacy and accuracy. MOSTLY AI's synthetic data generator offers automated, interactive privacy reports for each generated dataset, making it easy and fast to gain insight into the quality of synthetic data.