Synthetic Data Governance for Collaborative Social Research

Amelia J. Foster

Synthetic Data Governance for Collaborative Social Research

Author(s)	Dr. Amelia J. Foster
Country	United States
Abstract	Collaborative social research increasingly depends on access to administrative, survey, population, behavioral, and linked datasets that may contain confidential or personally sensitive information. Conventional restrictions on microdata access can protect participants but may simultaneously constrain multidisciplinary collaboration, methodological experimentation, reproducibility, researcher training, and cross-institutional innovation. Synthetic data offer a potentially valuable intermediary by generating artificial records from statistical or computational models designed to preserve selected properties of protected source data without directly reproducing the complete original records. The Office for National Statistics recognizes synthetic data as potentially useful for research, testing, processing, and statistical purposes when real data are inaccessible, while explicitly warning that synthetic datasets do not necessarily reproduce all properties of source data and require disclosure assessment before wider release. The UK Information Commissioner's Office similarly emphasizes that synthetic data may or may not be anonymous and that privacy risk should be assessed rather than assumed away merely because records are synthetic. This study develops a governance framework for synthetic data used in collaborative social research. The paper integrates privacy and disclosure control, statistical utility, representativeness, provenance, purpose limitation, researcher accountability, reproducibility, and cross-institutional access governance. A conceptual-methodological design is combined with a transparent simulation-based assessment of six governance-risk domains. The analysis identifies privacy and disclosure risk as the largest simulated contribution to overall governance exposure, followed by representativeness and bias, provenance and documentation, secondary use, purpose alignment, and reproducibility. These values are explicitly illustrative rather than empirical estimates. The study proposes a governance architecture that combines synthetic-data classification, use-case specification, privacy testing, utility validation, dataset documentation, controlled access, research-ethics oversight, human accountability, and validation against protected source data before consequential findings are published. The paper concludes that synthetic data should not be treated as inherently anonymous, inherently representative, or automatically suitable for unrestricted open science. Their value lies instead in enabling proportionate and auditable research access when technical protections are integrated with institutional governance.
Keywords	synthetic data, social research, data governance, privacy-preserving data sharing, research collaboration, statistical disclosure control, data utility, reproducibility, research ethics, differential privacy
Field	Engineering
Published In	Volume 3, Issue 1, January-February 2022
Published On	2022-01-21

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American Journal of Advanced Multidisciplinary Innovation and Research

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Synthetic Data Governance for Collaborative Social Research

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