GDPR and HIPAA Aware Synthetic Datasets, What to Know
Teams building AI agents and models hit a wall: they need realistic interaction data, but real-world logs often contain sensitive identifiers or are legally restricted. Use of real user data can trigger privacy investigations, incur compliance costs, and slow down model iteration. Synthetic data is the answer: it captures real behaviors and workflows without exposing real identities or medical records. But not all synthetic data is created equal, especially when regulations like GDPR and HIPAA are involved.
What synthetic data actually means
Synthetic data is generated from statistical models trained on real data. The models learn the distribution of features, relationships, and sequences. New samples are then drawn from that learned distribution, producing records that look realistic but contain no actual individuals or patients. For computer use tasks, this means synthetic mouse movements, clicks, and navigation paths that mimic human behavior. The key is that the underlying data never leaves the training set.
GDPR and the right to be forgotten
Under GDPR, individuals have the right to be forgotten and the right to data portability. Synthetic data helps because it does not contain personal data. However, GDPR also applies to any information that can identify a person, indirectly or directly. If a synthetic dataset is too accurate or too detailed, it might be re-identified. High-fidelity synthetic data for location, device IDs, or time-of-day patterns can sometimes allow reconstruction of individuals. To stay compliant, teams often apply differential privacy or add noise to synthetic records, which reduces accuracy but improves privacy.
HIPAA and protected health information
HIPAA regulates the use and disclosure of protected health information (PHI). Synthetic data can be designed to have no PHI. If you train on real EHR data, you must scrub identifiers and de-identify according to HIPAA Safe Harbor or Expert Determination rules. Synthetic data sidesteps many of those steps because the output never contains real PHI. Yet, if the synthetic dataset is highly granular and includes clinical codes, demographics, or temporal sequences that are unique, it might still be considered PHI. The safest approach is to validate synthetic outputs against privacy risk assessments and, when in doubt, use lower-fidelity or aggregated synthetic data.
Accuracy versus privacy tradeoffs
Synthetic data sits on a spectrum between high privacy and high utility. At one end, low-fidelity synthetic data uses broad categories and simple distributions. It is safe but often too noisy for training precise models. At the other end, high-fidelity synthetic data preserves fine-grained patterns and rare events, which improves model performance but increases re-identification risk. Teams must choose a point on this spectrum based on their privacy constraints and model requirements. Differential privacy, k-anonymity, and l-diversity are common techniques to inject privacy budgets into synthetic datasets.
Practical steps for compliant synthetic datasets
1. Define privacy boundaries before generating data. Decide what information is permissible and what must be excluded. 2. Train generative models on real data that has already been de-identified and anonymized. 3. Apply differential privacy or other privacy-preserving techniques to the synthetic outputs. 4. Evaluate re-identification risk using tools that check linkage to external datasets. 5. Validate model performance on both real and synthetic data to ensure synthetic data is not hurting accuracy.
The right synthetic data balances regulatory compliance with the fidelity needed for accurate AI training and evaluation.
How Coasty fits
Coasty runs computer use agents on real desktops and browsers to capture realistic interaction data. This enables the creation of synthetic datasets that reflect genuine workflows. Coasty’s synthetic data is produced through custom, contact-led engagement: you define your data requirements and compliance boundaries, and Coasty’s team designs and delivers bespoke datasets that align with your privacy and performance goals.
If you need synthetic data that respects GDPR and HIPAA without compromising on realism, schedule a data call with the Coasty team to discuss your requirements and see how they can help. Book a data call at https://cal.com/coasty/coasty-data-call.