- Data anonymization is a task that is increasingly challenging to perform. Anonymizing data successfully is practically impossible for complex datasets. As few as 15 characteristics are enough to reidentify 99.98% of the people in the US.
- Protecting data from leaking in the first place is almost impossible 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 on average unrestricted access to 11 million files on their first day of work.
- Old data anonymization approaches, like randomization, permutation, and generalization, destroy feature correlations and render the data useless for training machine learning models.
- Pseudonymization or de-identification is not data anonymization from a legal standpoint. Pseudonymized data is still personal data and is very vulnerable to privacy attacks.
- Training enterprise AI systems on production data or poorly masked data comes with serious security concerns. 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 (PII) from datasets. It is often done using legacy approaches, like masking or generalization. But they come with big downsides: weak privacy and low data utility. Synthetic data generation offers a better way to anonymize data with stronger privacy and more accurate data.
Data anonymization is becoming more and more challenging
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 often work with highly sensitive production data, regardless of the risks involved.
The data anonymization solution - synthetic data
Synthetic datasets provide a secure alternative to original data by ensuring privacy and compliance with privacy regulations like the General Data Protection Regulation (GDPR). These artificial data points are engineered to serve as direct substitutes for real data in various downstream applications. Generative AI models learn the patterns and statistical attributes of the original data and then are used to re-create new - entirely made up - datasets. These synthetic datasets "look and feel" like the original data and contain all the statistical information, but none of the personal identifiable information.
The ability to maintain statistical characteristics makes synthetic data an exceptionally useful resource for scenarios that demand high-quality data. 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 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 representative 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 Platform offers automated Model and Data Insights reports making it easy and fast to gain insight into the quality of synthetic data.
You can learn more about how our Platform ensures privacy of the generated synthetic data here.
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.