- 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.
- Training enterprise AI systems on production data or masked data comes with serious security concerns and due to the data loss resulting from legacy data anonymization, the performance of AI could also suffer.
What is data anonymization?
Data anonymization is the process of removing or obfuscating personally identifiable information from datasets. It 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. The adoption of third-party AI systems, like LLMs, also requires the injection of domain or enterprise-specific knowledge in the form of sensitive data. Legacy data anonymization tools destroy this domain knowledge and produce heavily masked data that is not suitable for training AI systems.
The data anonymization solution
Synthetic datasets provide a secure alternative to raw data by ensuring both privacy and compliance with the General Data Protection Regulation (GDPR). These artificial data points are engineered to serve as direct substitutes for real data in various downstream applications. During the process of data synthesization, essential statistical attributes such as means, variances, and correlations are meticulously retained. Moreover, the synthetic data maintains referential integrity across multiple datasets, ensuring that relationships between tables or collections are preserved.
The ability to maintain statistical characteristics makes synthetic data an exceptionally useful resource for scenarios that demand high-quality intelligence. For example, in machine learning development, having a reliable yet privacy-safe dataset is crucial for training robust models. Similarly, synthetic data enables data democratization—the practice of making data accessible to non-technical users—by allowing more people to engage with the data while ensuring that no sensitive information is exposed. All these advantages come without sacrificing compliance with stringent data protection laws, making synthetic data an increasingly popular choice for organizations.
According to the European Union's Joint Research Center, the implications of synthetic data are far-reaching: "Synthetic data changes everything from privacy to governance." This statement underscores the transformative potential of synthetic data in reshaping how we approach not only data privacy but also broader issues of data management and governance.
Data anonymization with empirical proof
All synthetic datasets generated on MOSTLY AI's platform come with an automated data privacy report, giving you actionable insights on the sufficiency of data anonymization. These privacy metrics include Identical match share, Distance to closest record and the Nearest neighbor distance ratio.
- Identical match share (IMS) checks for the share of exact matches between synthetic and original samples.
- Distance to closest record (DCR) measures the distances between synthetic samples and their closest corresponding original record.
- Nearest neighbor distance ratio (NNDR) measures the individual-level DCR as well, but normalizes these with the DCRs of further neighbors, to better account for outlier areas.
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.
Case studies and guides
Ready to try synthetic data?
The best way to learn about synthetic data is to experiment with synthetic data generation. Try it for free or get in touch with our sales team for a demo.