A review of synthetic data terminology for privacy preserving use cases
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Summary
This summary is machine-generated.Standardized terminology is crucial for the secure generation and use of synthetic data in research. This paper proposes definitions for key terms like synthetic data, utility, utility measure, and fidelity to improve consistency and sharing.
Area Of Science
- Health Informatics
- Data Science
- Privacy-Preserving Technologies
Background
- Synthetic data generation is vital for research using secure administrative and health data.
- Lack of standardized terminology impedes quality standards, governance, and knowledge sharing for synthetic data.
- This is particularly critical for synthetic data derived from protected personal information.
Purpose Of The Study
- To review existing literature on synthetic data terminology.
- To explore current definitions and identify inconsistencies, especially for privacy-preserving use cases.
- To propose standardized definitions for key synthetic data terms.
Main Methods
- Literature review of synthetic data generation and terminology.
- Analysis of term definitions in the context of privacy-preserving applications.
- Development of proposed broad definitions for core terminology.
Main Results
- Existing terminology for synthetic data properties is often inconsistent and lacking.
- The diversity of synthetic data types and use cases complicates universal definitions.
- Privacy-preserving synthetic data presents unique definitional challenges due to protected source data characteristics.
Conclusions
- Consensus on terminology is essential for advancing synthetic data research and application.
- Clearer descriptions in future literature should specify data intended use and evaluation measures.
- Proposed definitions for synthetic data, utility, utility measure, and fidelity aim to foster consistency.
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