Synthetic data or anonymized data: which method should you choose?

Protecting privacy has become a central issue for all organizations that use data. In this context, anonymized data has emerged as a go-to solution.

Data that's been anonymized from actual datasets provides a degree of trustworthiness and practical applicability that purely synthetic data hasn't yet been achieved. Here's the reasoning.

Anonymized data (when the tool preserves referential integrity) keeps exactly the same univariate, bivariate, and multivariate distributions as the original data.

Synthetic data, even when generated by the best models (GANs, VAEs, diffusion models, copulas, etc.), always introduces approximation bias. Rare or complex correlations are systematically smoothed out or lost.

→ In practice, anonymized data produces the same results as raw data, while synthetic data often reduces model performance.

DOT Anonymizer and robust referential anonymization tools perfectly preserve links between tables (foreign keys, cardinalities, business rules).

Synthetic data generators, on the other hand, struggle to reproduce complex inter-table consistency without introducing incoherence (e.g., a patient with an appointment in 2026 but a recorded death date in 2025).

→ Inconsistencies are common in synthetic data, but nearly nonexistent with data produced by referential anonymization tools.

Synthetic models can generate values that are highly improbable or outright impossible in a business domain (e.g., a €250,000 salary for a 19-year-old in certain sectors or a blood pressure reading of 300/200, and so on.).

Advanced, consistent anonymization preserves domain constraints and management rules (no out-of-range values possible).

Compliant anonymization (k-anonymity, l-diversity, t-closeness, optional differential privacy) can move data outside the scope of “personal data” (GDPR Recital 95).

Synthetic data, even if it no longer contains real records, is often still considered personal data when trained on real personal data (principle of “possible reconstruction” — see the CNIL and EDPB positions/decisions).

Auditors and authorities like CNIL or FDA generally accept a well-documented anonymization process far more readily than a synthetic dataset whose biases are not fully controlled.

→ Legal risk is limited when using anonymized data, whereas synthetic data does not ensure regulatory compliance in many sectors (healthcare, banking, insurance).

Choosing advanced anonymization is less expensive than generating a synthetic dataset of the same volume and complexity.

→ No need to train or tune a complex generative model.

Synthetic data generation typically requires longer, more complex implementation, including statistical modeling and extensive calibration phases. Costs are higher and less predictable, with significant upfront investment and slower ROI.

Consistent anonymization using widely proven tools like DOT Anonymizer enables fast implementation via masking-rule configuration. Costs are controlled and predictable, with limited upfront investment and fast ROI (deployment in weeks and immediate gains through risk reduction).

Additionally, licensing for synthetic data generation solutions are generally more expensive than anonymization solutions,; making tools like DOT Anonymizer a more cost-effective choice.

“Consistent anonymized data faithfully reproduces statistical and business reality with no risk of hallucination, whereas synthetic data—despite its progress—remains an inevitably imperfect approximation of that reality.”

In conclusion, while synthetic data is an innovative approach to privacy protection, dependable anonymized data is often more reliable, easier to manage, and offers stronger regulatory compliance. For accurate analytics, smooth integration into complex systems, and maximum regulatory alignment; anonymized data processed with a tool like DOT Anonymizer remains the most reliable and relevant choice.