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Building Synthetic Versions of Confidential Social Science Data

Learn about creating synthetic versions of confidential social science data to preserve privacy while maintaining data validity. Explore examples like Origin-Destination Commuter Data and resources for this innovative process.

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Building Synthetic Versions of Confidential Social Science Data

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  1. Building Synthetic Versions of Confidential Social Science Data John M. AbowdEdmund Ezra Day Professor, CornellDistinguished Senior Research Fellow, US Census Bureau

  2. Outline • What are synthetic data? • The feedback cycle • Example: Origin-Destination Commuter Data • Example: Demographic Data • Example: Employer-Employee Matched Data • How do you do it? • Resources

  3. What Are Synthetic Data? • Micro data constructed by sampling from appropriate distributions to simulate a target population • Preserve the multivariate distribution of the underlying confidential data (along all dimensions) by appropriate modeling • Protect the confidentiality of the underlying data by releasing only the synthetic version

  4. Conceptual Framework D • The mission of data providers • Disseminate information • Protect confidentiality • Efficient production is on the PPF (not Z) • X is optimal when preferences favor dissemination • Y is optimal when preferences favor protection • X and Y are both feasible X Y Z P

  5. The Research – Synthetic Data Feedback Cycle ConfidentialityProtection ScientificModeling DataSynthesis AnalyticValidity

  6. Example: Origin-Destination Data • Block-level transportation analysis of commuting patterns • Origin household characteristics (including address) protected by synthetic data methods • Destination establishment characteristics protected by dynamic noise infusion (see example 3) • Released in prototype by the LEHD Program at the Census Bureau • http://lehd.dsd.census.gov/led/datatools/onthemap.html

  7. Where do workers working between Minneapolis/St Paul live?

  8. Example: Demographic Data • Survey of Program Participation linked to all available Social Security Administration earnings and benefit data • Joint project of LEHD at Census and SSA ORES • Protected by partial synthetic data • Scheduled for release within a year

  9. Example: Longitudinally Linked Employer-Employee Data • LEHD Program Quarterly Workforce Indicators • Current releases protected by dynamic noise infusion • Future releases will be protected by synthetic data methods • http://lehd.dsd.census.gov/led/datatools/qwi-online.html

  10. How Do You Do It? • Assemble all data in a protected, confidential enclave (e.g., US Census Bureau) • Identify important relations to preserve • Model • Synthesize • Test • Repeat until the results are satisfactory • User feedback improves future versions

  11. Resources • John Abowd’s Social and Economic Data Course • LEHD Program Technical Papers • NSF-ITR Virtual Research Data Center

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