Synthetic Data Under GDPR: Compliance Challenges

Introduction

Synthetic data generation under the General Data Protection Regulation presents complex compliance challenges that directly impact how organisations process personal data for the development of artificial intelligence. Whether synthetic data generated from personal datasets qualifies as personal data itself depends on the effectiveness of anonymisation and re-identification risks; a distinction that determines the full scope of GDPR obligations.

The legal status of synthetic datasets hinges on whether they enable the identification of specific data subjects, making anonymisation techniques and technical safeguards critical for compliance strategies.

What This Guide Covers

This guide covers the applicability of the GDPR to synthetic data generation, anonymisation requirements under data protection law, re-identification risk assessment, and practical compliance frameworks for organisations processing personal data. We focus on legal requirements and implementation strategies, not technical data generation methods or algorithmic details.

What You’ll Learn:

• When GDPR applies to synthetic data generation and processing activities
• Anonymisation vs pseudonymisation distinctions for determining personal data status
• Compliance frameworks for lawful basis, data subject rights, and technical safeguards
• Risk assessment methodologies for re-identification and data protection impact assessments

Understanding Synthetic Data and GDPR Fundamentals

Synthetic data refers to data artificially generated through computer algorithms or machine learning models that statistically mimic the properties of original datasets without recording actual events involving identified or identifiable individuals.

The General Data Protection Regulation applies to the processing of personal data, information relating to an identified or identifiable natural person. This definition determines whether synthetic datasets fall within the scope of the GDPR based on their potential for re-identification, rather than their method of artificial generation.

Understanding when synthetic data constitutes personal data is essential because it triggers the full range of GDPR obligations, from lawful basis requirements to data subject rights and technical and organisational measures for data protection.

Types of Synthetic Data Under GDPR

Fully synthetic data consists of entirely artificial records generated through machine learning models without direct correspondence to real individuals. When properly anonymised, such data may fall outside the GDPR scope as non-personal data.

Partially synthetic data combines fundamental data elements with artificially generated components, creating hybrid datasets that often retain sufficient detail to enable identification of particular consumers or data subjects.

This classification connects directly to GDPR compliance because fully synthetic data that achieves adequate anonymisation avoids regulatory obligations, while partially synthetic data typically remains personal data subject to the complete data protection regulation framework.

Anonymisation vs Pseudonymisation Framework

GDPR Recital 26 establishes that personal data rendered anonymous through irreversible processes ceases to be personal data and falls outside the scope of the GDPR. The European Union’s Article 29 Working Party Opinion 05/2014 provides detailed criteria for adequate anonymisation, requiring that re-identification becomes impossible through any reasonably likely means.

Pseudonymisation, as defined in GDPR Article 4(5), involves replacing identifying elements with artificial identifiers while maintaining the possibility of re-identification with additional information. Pseudonymised data remains personal data under data protection laws, requiring continued GDPR compliance.

Building on synthetic data classification, these anonymisation standards determine whether the resulting synthetic data generates new compliance obligations or qualifies for exemption as anonymous data with reduced privacy risks.

Understanding these foundational concepts enables evaluation of specific GDPR compliance requirements that apply when organisations generate synthetic data from personal datasets.

GDPR Compliance Requirements for Synthetic Data

Organisations must address GDPR compliance obligations when synthetic data generation involves processing original personal data, regardless of whether the resulting synthetic data achieves anonymisation status.

Lawful Basis Requirements

Article 6 GDPR requires a lawful basis for processing personal data to create synthetic datasets, with legitimate interests often cited as a basis for research and development activities involving training data. Data controllers must demonstrate that synthetic data generation serves compelling organisational interests while implementing appropriate safeguards for data subject rights.

Special category data, as outlined in Article 9, requires explicit consent or specific legal grounds when generating synthetic health data or other sensitive information. The heightened protection for sensitive data applies additional restrictions on processing activities and technical and organisational safeguards.

Organisations processing customer data for synthetic data generation must document lawful basis assessments and ensure compliance with data minimisation principles throughout the data synthesis process.

Data Subject Rights Implications

Access, rectification, and erasure rights under GDPR Articles 15-17 apply when synthetic datasets retain identifiability of specific data subjects. Data controllers must establish procedures for handling data subject requests related to both underlying data and resulting synthetic data.

Unlike fully anonymised synthetic data, partially synthetic datasets that enable identification maintain the complete range of data subject rights obligations, including portability and objection rights under Articles 20-21.

The right to explanation under automated decision-making provisions may extend to synthetic data used in training AI models that affect individual data subjects, requiring transparency about synthetic data sources and generation methods.

Re-identification Risk Assessment

Technical and organisational measures must address re-identification risks through differential privacy, k-anonymity, or other privacy-enhancing technologies that demonstrably reduce the possibilities of identification. The European Data Protection Supervisor emphasises the need for continuous risk monitoring as synthetic data models evolve.

Risk thresholds require regular evaluation as new re-identification techniques emerge and statistical properties of synthetic datasets become better understood through advances in machine learning and data analytics.

Data protection authorities expect organisations to document their risk assessment methodologies and demonstrate that technical safeguards achieve practical anonymisation, rather than merely theoretical privacy protection.

These compliance requirements form the foundation for implementing comprehensive synthetic data programs that strike a balance between innovation objectives and data protection obligations.

Implementing GDPR-Compliant Synthetic Data Programs

Effective implementation requires systematic approaches that address legal requirements while enabling practical use of synthetic datasets for training machine learning models and supporting data-driven technologies.

Step-by-Step: GDPR Compliance Framework

When to use this: Organisations planning to generate synthetic data from personal datasets for AI development, analytics, or data sharing initiatives.

1. Conduct Data Protection Impact Assessment (DPIA): Evaluate processing activities, identify privacy risks, and document safeguards for synthetic data generation under GDPR Article 35 requirements.

2. Implement Privacy Enhancing Technologies: Deploy differential privacy, k-anonymity, or other technical measures that reduce re-identification risks while preserving the statistical distribution of training data.

3. Establish Re-identification Risk Monitoring: Create ongoing assessment procedures that evaluate identification risks as synthetic data generators improve and new re-identification techniques emerge.

4. Document Compliance Measures: Maintain processing records under Article 30, including lawful basis justification, technical safeguards, and data subject rights procedures for synthetic datasets.

Comparison: Anonymised vs Pseudonymised Synthetic Data

Feature	Anonymised Synthetic Data	Anonymised Synthetic Data
GDPR Status	Non-personal data (exempt)	Personal data (regulated)
Data Subject Rights	Not applicable	Full rights obligations
Retention Limits	No GDPR restrictions	Article 5 limitations apply
Transfer Requirements	No Article 44 restrictions	Chapter V safeguards required
Risk Level	Pseudonymised Synthetic Data	Moderate to high

Organisations should prioritise anonymised synthetic data approaches when statistical properties can be preserved without enabling re-identification, while accepting pseudonymised approaches only when business requirements justify continued compliance with GDPR obligations.

Despite systematic implementation approaches, organisations commonly encounter specific challenges that require targeted solutions for effective compliance.

Common Challenges and Solutions

Organisations implementing synthetic data programs face recurring compliance obstacles that can compromise both privacy protection and innovation objectives without proper resolution strategies.

Challenge 1: Determining Personal Data Status

Problem: Legal uncertainty about whether specific synthetic datasets qualify as personal data under GDPR, particularly when advanced machine learning models generate highly realistic synthetic records.

Solution: Implement identifiability assessment frameworks using Article 29 Working Party criteria combined with regular re-identification testing by independent data analysts. Document assessment methodologies and maintain evidence that synthetic data stands outside reasonable identification possibilities.

Challenge 2: Balancing Data Utility and Privacy

Problem: Privacy-enhancing technologies that reduce re-identification risks may significantly compromise the statistical accuracy required for practical training of AI models and data analysis applications.

Solution: Apply differential privacy with calibrated noise levels that optimise utility-privacy tradeoffs, and implement iterative testing to identify minimum privacy parameters that preserve essential statistical properties of real-world data while achieving anonymisation thresholds.

Challenge 3: Managing Cross-Border Data Transfers

Problem: The international transfer of synthetic datasets created from EU personal data raises questions about the adequacy requirements of Chapter V and the appropriate safeguards under data protection legislation.

Solution: Ensure effective anonymisation before international transfers to qualify as non-personal data, or implement Standard Contractual Clauses and additional safeguards when synthetic datasets retain identifiable information, thereby requiring continued GDPR protection.

Addressing these challenges requires ongoing attention to regulatory developments and technical advances that continue shaping synthetic data compliance requirements.

Conclusion

GDPR compliance for synthetic data fundamentally depends on achieving effective anonymisation that eliminates re-identification risks while preserving data utility for machine learning and analytics applications. Organisations must treat synthetic data generation as a regulated processing activity requiring systematic risk management and technical safeguards.

Additional Resources

Regulatory Guidance: European Data Protection Supervisor opinions on privacy-enhancing technologies, Article 29 Working Party Opinion 05/2014 on anonymisation techniques, and national data protection authority guidance on synthetic data classification under GDPR.

Technical Standards: ISO/IEC 27559 privacy engineering standards, NIST Privacy Framework considerations for synthetic data, and IEEE standards for differential privacy implementation in synthetic data generators.