Why Is Synthetic Data Gaining Legal Attention?
Synthetic data—artificially generated data mimicking real-world data patterns without containing actual personal information—is increasingly used for AI training, software testing, research and development, and compliance with data minimization principles. Technologies like generative AI enable creating realistic synthetic datasets preserving statistical properties while protecting individual privacy.
Synthetic data offers potential benefits including reduced privacy risks by avoiding real personal data, easier data sharing without consent requirements, cost savings from generating rather than collecting data, and ability to create balanced datasets addressing bias.
However, synthetic data creates complex legal questions around whether synthetic data truly anonymizes individuals, what rights exist in synthetic datasets, how to validate privacy protections, and compliance with regulations requiring real data.
Companies using or creating synthetic data must understand when synthetic data constitutes personal data under privacy laws, intellectual property rights in synthetic datasets, regulatory acceptance for compliance purposes, and best practices for responsible synthetic data generation.
Definition and Creation of Synthetic Data
What Qualifies as Synthetic Data
Synthetic data is artificially generated data created through algorithms, statistical models, or generative AI that mimics characteristics of real data without containing actual observations.
Synthetic data can include synthetic personal information like names, addresses, and demographic attributes, synthetic financial transactions, synthetic medical records, synthetic images or videos, and synthetic text or communications.
Generation Methods
Common synthetic data generation approaches include statistical simulation using probability distributions, generative adversarial networks (GANs) learning to create realistic data, variational autoencoders encoding and reconstructing data patterns, and agent-based modeling simulating behaviors.
The generation method affects privacy risks and data utility.
Data Utility vs. Privacy Trade-Off
Synthetic data involves fundamental trade-offs between utility maintaining statistical properties needed for purposes and privacy ensuring individuals cannot be identified or inferred.
More realistic synthetic data creates better utility but higher privacy risks.
GDPR and Personal Data Classification
When Is Synthetic Data Personal Data
GDPR defines personal data as information relating to identified or identifiable individuals. Synthetic data can constitute personal data if it allows identification of individuals, is generated from personal data without adequate anonymization, or when combined with other data enables identification.
Mere artificial generation doesn’t automatically make data non-personal.
Anonymization Standards
GDPR treats truly anonymized data as outside its scope. For synthetic data to be anonymized, individuals must not be identifiable through singling out individuals from datasets, linking records to identify individuals, or inferring information about individuals.
Anonymization must be irreversible with reasonable means.
GDPR Article 29 Working Party Guidance
The Article 29 Working Party emphasized that anonymization must withstand robust attacks including by combining datasets, using technical capabilities like computational power, and exploiting any residual information.
Synthetic data must be evaluated against these standards.
Re-Identification Risks
Membership Inference Attacks
Attackers can determine whether specific individuals’ data was in training sets used to generate synthetic data. This reveals participation in studies or databases that may be sensitive.
Membership inference demonstrates that synthetic data may leak information about individuals.
Attribute Inference
Even when specific individuals can’t be identified, synthetic data may reveal attributes about groups or enable inferring characteristics of individuals in source data.
Reconstruction Attacks
Sophisticated attacks can reconstruct training data from synthetic datasets or models trained on synthetic data, particularly when synthetic data closely resembles training data.
Differential Privacy Protections
Differential privacy provides mathematical guarantees limiting information leakage about individuals. Generating synthetic data with differential privacy provides provable privacy protections.
However, strong differential privacy may reduce data utility.
CCPA and State Privacy Laws
California Consumer Privacy Act
CCPA defines personal information broadly as information identifying, relating to, or reasonably linkable to consumers or households. Synthetic data derived from personal information may remain personal information if linkable to individuals.
CCPA provides exemptions for deidentified or aggregate consumer information if businesses implement technical safeguards, prohibit reidentification, and contractually obligate recipients not to reidentify.
Deidentification Standards
CCPA requires that deidentification renders information not reasonably linkable to consumers, households, or devices. Businesses must ensure information cannot be reassociated without additional information held separately, and take reasonable measures to ensure information is not reidentified.
Other State Laws
Virginia, Colorado, Connecticut, and other state privacy laws have similar provisions treating properly deidentified data as outside scope while requiring safeguards.
Intellectual Property Rights in Synthetic Data
Copyright in Synthetic Datasets
Copyright protects original works of authorship. Synthetic datasets may qualify for copyright if they reflect creative selection, coordination, or arrangement, even if individual elements aren’t copyrightable.
However, purely automated generation without creative input may lack copyrightability.
Database Rights
Some jurisdictions provide sui generis database rights protecting substantial investments in creating databases. EU database directive protects databases where creators made substantial investment in obtaining, verifying, or presenting contents.
Synthetic datasets may qualify if creators invested substantially in development.
Trade Secret Protection
Synthetic data generation methods, underlying models, or valuable synthetic datasets may qualify as trade secrets if they derive value from secrecy, aren’t generally known, and are subject to reasonable secrecy measures.
Ownership and Licensing
Clarify ownership of synthetic data through agreements with developers creating synthetic data, vendors providing generation services, and customers receiving synthetic data.
Licensing agreements should specify permitted uses, restrictions on reidentification attempts, and liability allocation.
Regulatory Acceptance
Clinical Trials and Healthcare
Healthcare regulators increasingly consider synthetic data for clinical trial designs, drug development research, and medical device testing.
However, regulators require validation that synthetic data accurately represents real patient populations and doesn’t introduce bias or underestimate risks.
Financial Services
Financial regulators evaluate synthetic data for model development, stress testing, and fair lending analysis.
Concerns include whether synthetic data captures tail risks or extreme scenarios adequately.
Government and Public Sector
Government agencies explore synthetic data for protecting sensitive information while enabling research. Census bureaus consider differential privacy and synthetic data for protecting respondent privacy.
Bias and Fairness Considerations
Replicating Existing Bias
Synthetic data generated from biased training data may perpetuate or amplify discrimination. If training data underrepresents minorities, synthetic data will reflect this.
Creating Balanced Datasets
Synthetic data enables deliberately creating balanced datasets addressing underrepresentation. Organizations can generate synthetic records for underrepresented groups.
However, purely synthetic representation may not capture real-world complexity.
Fairness Testing
Test synthetic datasets for bias across demographic groups, representativeness of populations, and disparate impact in downstream uses.
Transparency and Disclosure
Disclosing Synthetic Data Use
When using synthetic data for consequential decisions, consider disclosing that decisions rely on synthetic rather than real data, how synthetic data was generated and validated, and limitations or uncertainties.
Research and Publication
Academic research using synthetic data should disclose synthetic data use, generation methodology, validation approaches, and comparison to real data where possible.
Customer and User Transparency
If AI services are trained on synthetic data, inform users when appropriate, especially if this affects quality or reliability.
Contractual Considerations
Data Purchase and Licensing
Agreements for purchasing or licensing synthetic data should specify data characteristics and quality guarantees, whether data is truly synthetic or derived from personal data, permitted uses and restrictions, and liability for privacy violations if data enables reidentification.
Vendor Generation Services
When vendors generate synthetic data from client data, contracts should clarify ownership of synthetic datasets, restrictions on vendor use of client data and synthetic data, confidentiality obligations, and compliance with privacy laws.
Warranties and Representations
Providers should warrant that synthetic data doesn’t enable identification of individuals, complies with privacy laws, and was generated without violating third-party rights.
Customers should warrant lawful collection of source data if providers generate synthetic data from customer-provided datasets.
Best Practices for Synthetic Data Generation
Privacy by Design
Implement privacy protections throughout synthetic data generation including differential privacy in generation algorithms, k-anonymity or other privacy metrics, aggregation reducing granularity, and noise addition obscuring individual characteristics.
Validation and Testing
Validate synthetic data through privacy testing for reidentification risks, utility testing for statistical properties, bias analysis across groups, and comparison to real data holdouts.
Documentation
Document generation methodology and parameters, privacy protections and their strength, validation results, and limitations and intended uses.
Governance
Establish governance frameworks for synthetic data including ethical review of generation plans, oversight of reidentification risks, policies for data access and use, and incident response for privacy breaches.
Emerging Legal Developments
Regulatory Guidance
Privacy regulators are developing guidance on synthetic data including when synthetic data qualifies as anonymized, acceptable anonymization techniques, and validation requirements.
Standards and Frameworks
Industry and standards bodies are creating frameworks for responsible synthetic data including ISO standards for anonymization, NIST guidance on differential privacy, and sector-specific best practices.
Liability and Risk Management
Privacy Violation Liability
If synthetic data enables identification, creators face liability for privacy violations, unauthorized disclosure of personal information, and breach of contract if warranties about anonymization prove false.
Insurance Coverage
Cyber liability and privacy insurance should cover risks from synthetic data including reidentification incidents, regulatory enforcement for inadequate anonymization, and liability to individuals harmed by privacy breaches.
Conclusion: Navigating Synthetic Data Legal Landscape
Synthetic data offers privacy benefits but requires careful legal and technical implementation. Companies must evaluate whether synthetic data is truly anonymized under applicable laws, implement robust privacy protections during generation, validate privacy and utility continuously, and maintain transparency about synthetic data use.
As synthetic data adoption grows, legal standards will evolve through regulation, litigation, and industry practices.
Contact Rock LAW PLLC for Synthetic Data Legal Counsel
At Rock LAW PLLC, we help companies navigate legal issues with synthetic data and AI privacy compliance.
We assist with:
- Privacy compliance for synthetic data
- Anonymization and deidentification strategy
- Synthetic data contracts and licensing
- Intellectual property protection for datasets
- Regulatory guidance and validation
- Privacy risk assessment and mitigation
Contact us for strategic counsel on synthetic data legal implications.
Related Articles:
- Privacy Laws and AI Training Data
- Cross-Border Data Transfers for AI Systems
- Algorithmic Bias and Discrimination Compliance
Rock LAW PLLC
Business Focused. Intellectual Property Driven.
www.rock.law/