Do you have a question about synthetic data, Statice, or the Statice software? Here are some common answers that should help.
Let's answer some questions.
What is privacy-preserving synthetic data?
Synthetic data is data artificially generated that mimics the structure and statistical properties of data gathered from real-life events. Synthetic data in itself isn’t enough to guarantee privacy. However, it can be coupled with privacy mechanisms that guarantee, upon releasing it, the protection of sensitive information present in the original data. The result is privacy-preserving synthetic data, and it is the focus of our work at Statice. You can read more in this article on our blog.
What can my company do with privacy-preserving synthetic data?
The nature of privacy-preserving synthetic data makes it a suitable candidate for multiple use cases along the data lifecycle. You can use synthetic data as a drop-in replacement for real data in analysis workflows, like statistical analysis or machine learning modeling. The privacy protections in place also allow you to share data internally or with third-party partners. You can read about more use-cases in this article.
Can you guarantee the anonymity of the synthetic data?
Anonymization refers to the process of irreversibly transforming data to prevent the re-identification of individuals, whether directly or indirectly. It represents the highest form of privacy protection. Perfect anonymity of data is rarely achieved, as it would render the data almost useless. However, we implemented at Statice best-in-class privacy research to provide the maximum protection and transparency levels while maintaining a sufficient data utility.
The Statice software lets you ensure that its machine learning models satisfy the definition of Differential Privacy. This mathematical guarantee offers the most robust privacy protections against re-identification. Additionally, you access empirical and adversarial privacy and risk evaluations proving that no synthetic record can be linked to records from the original data.
How well does your synthetic data capture the quality of the original data?
Internal and external tests showed that the Statice synthetic data offers one of the highest utility on the market. The Statice software generates privacy-preserving synthetic data that reproduces high-order statistical properties of the original data.
It can maintain to a high similarity degree marginal distributions, conditional distributions, aggregated statistics, dates and times distributions and pairwise dependencies (e.g., correlations and mutual information) in the data.
Is Statice’s synthetic data GDPR compliant?
Yes. Several Data Protection Offices in Europe, notably in Germany and France, stated that synthetic data with strong enough privacy guarantees, such as the ones offered by Statice, is anonymous and thus fall outside of the scope of the GDPR.
What is the difference between synthetic data and pseudonymized data?
The fact that it is possible to reverse data masking or pseudonymization techniques is a significant technical difference. Pseudonymization methods complicate individuals' identification but never entirely remove the re-identification risks, making them weaker privacy protection mechanisms.
Modern data privacy laws, such as the GDPR and the California Consumer Privacy Act (CCPA), legally differentiate between pseudonymization and anonymization. Properly anonymized data isn't subject to personal data protection laws anymore. On the other hand, pseudonymized data still represents a risk for individual privacy and must be handled as personal data.
Can I test your software?
Yes. We strongly believe that the best way to decide if a tool suits a specific use case is to test it “in the wild” with real data, and real infrastructure. Real world use tends to bring out all the challenges of a particular use case, and so we try to enable our clients to test the software on their projects, with support from our experts, during the buying process. Generally this takes the form of a feasibility study, or a proof of concept project. For more information on how this works, don’t hesitate to contact us to discuss your project.
Still got questions?
Reach out to our team. We would be happy to answer any questions you might have or provide custom material to support your project.