Statice is Now part of Anonos Data Embassy Platform

Statice's synthetic data technology is now part of Anonos Data Embassy, the award-winning data security and privacy solution.


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synthetic data

What is synthetic data?

Synthetic data is artificially generated data that mimics the structure and statistical properties of data gathered from real-life events.

It's possible to produce partially synthetic data, where only some synthetic data points complement an existing dataset. A fully synthetic dataset doesn't contain any original data. 

Structured and unstructured datasets can serve as a basis for generating synthetic tabular or image data.

Finally, privacy mechanisms can be added during the generation process to protect sensitive information in the original data. 

Synthetic data FAQs

synthetic data generate

Organizations or teams can replace unavailable data with artificially generated datasets when they don't have enough data for a given use case.

In contexts where data exists but its processing is restricted by regulations such as the GDPR, synthetic data with anonymization guarantees becomes a compliant alternative. Synthetic data can also be an alternative when security constraints prevent data from flowing through an organization. 

Finally, synthetic data can be more economical when real data is too costly to acquire or produce.

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synthetic data

Generating synthetic data comes down to learning the joint probability distribution in an original, real dataset to generate a new dataset with the same distribution.  The more complex the real dataset, the more difficult it is to map dependencies correctly. Deep learning models such as generative adversarial networks (GAN) and variational autoencoders (VAE) are well suited for synthetic data generation.

The Statice software follows a hybrid approach to synthetic data generation. It breaks down data into groups and handles each one with the model best suited to its characteristics. 

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synthetic data privacy

A common misconception is to think that synthetic data is inherently private. On the one hand, the models that generate synthetic data might memorize and reproduce features from the training data, ultimately leading to privacy leaks. On the other hand, synthetic data is vulnerable to privacy attacks that might lead to the re-identification of individuals in the original data. 

Different mechanisms are available to mitigate privacy risks. You can choose to train the model using algorithms that satisfy the definition of Differential Privacy. It’s also possible to use privacy evaluations to measure re-identification risks and assess the probability of information leakage.

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synthetic data applications

Organizations in user-centered and regulated industries must be able to develop their data operations while respecting data protections such as the GDPR.

Synthetic data applications stretch along the data lifecycle: from data integration to data dissemination. Popular applications include cloud migration, internal data sharing, data retention, data analysis, data testing, AI/ML model training, 3rd party data sharing, product development, data monetization, or data publication.

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The benefits of using synthetic data

Protecting privacy and utility: Data access is a strategic topic for all businesses. With the GDPR coming into force, privacy also became a cornerstone of data strategies as organizations followed new compliance regulations and security requirements. 

To competitively fuel the development of AI applications, build personalized customer experiences and develop competitive products, organizations must answer the modern data challenges posed by compliance and security constraints, data silos, and innovation needs.

Increase data agility

Contrary to real-world data, your teams can access anonymized synthetic data without going through lengthy validation processes. Your organization’s data becomes more agile with synthetic data.

Anticipate regulatory changes

We’ve built auxiliary protection mechanisms and privacy tools that let you document the anonymity of privacy-preserving synthetic data. You avoid the financial risks of non-compliance and the security risks related to data re-identification.

Unlock new use-cases

Personal data processing imposes security and compliance restrictions that don't apply to anonymized synthetic data so that you can unlock data applications, from data retention to external sharing, without compromising privacy.

Ready to go synthetic?

Present us your data challenges, and see how we can help!