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Data anonymization - synthetic data for maximum privacy and utility

Data anonymization is often done using legacy tools, like data masking, endangering privacy, and destroying data utility. Synthetic data generation offers a better way to anonymize data without losing any intelligence locked up in datasets.

Data anonymization
challenges

The status quo in data anonymization

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.

data anonymization vs synthetic data

The data anonymization solution 

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."

Data anonymization with synthetic data best practices

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.  

 

Quality assurance report for synthetic data

Case studies and guides

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