Synthetic data is artificial data generated from original data using a model trained to reproduce its characteristics and structure.
Overcome challenges related to cybersecurity, privacy, and data sensitivity while using advanced information modeling and AI techniques.
Managing your data governance
Synthetic data can work as a Privacy Enhanced Technology (PET), applying data protection by design in cases involving personal data. Synthetic data offers a solution to overcome the limitations of access to real data, allowing to test, train algorithms, and develop applications without exposing sensitive information. In the development, testing, and validation of machine learning services, where actual data is not available in sufficient quantities, synthetic data plays a crucial role.
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- GDPR compliance. Ensure data privacy beyond simple anonymisation or avoiding the need to aggregate data shared with suppliers. Strike a privacy-utility balance, allowing suppliers and service providers to develop their services and analytics appropriately.
- Work with synthetic data. The “new” synthetic data we create will provide the same useful information as the original data with the advantage that is not revealing any sensible data and reducing the risk in case of a cybersecurity failure.
- Avoid sensitive information can be inferred from shared data. We overcome the frequent challenge of avoiding the recognition of people based on behaviours or related data, even if data is already anonymised. For instance: “John goes to the children’s hospital by motorbike the 2nd of July”. Even if we remove the ID or name of the data, we could easily identify him.
Managing privacy while extracting data value
Our solution balances privacy and utility. We overcome the limitations of traditional anonymisation, avoiding both direct identification and indirect inferences.
For example, our synthetic data allows us to maintain the integrity of critical patterns and trends, which is essential for predictive analysis or identifying behaviors without compromising individual privacy.
Two illustrative use cases could be:
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- Urban planning: synthetic data makes it possible to model mobility patterns without revealing the exact location of individuals, thus protecting their privacy while optimising transportation systems.
- Healthcare: synthetic data can allow a better personalisation of treatments while protecting patient healthcare history information as well as their behaviour data. This would generate better results, treatment adherence as well as train future models to assist healthcare professionals in advanced diagnosys.
In essence, our synthetic data solution is a valuable tool for balancing privacy and utility when processing data.
