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Confidentiality Protection of Social Science Micro Data: Synthetic Data and Related Methods John M. Abowd Cornell University and Census Bureau January.

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Presentation on theme: "Confidentiality Protection of Social Science Micro Data: Synthetic Data and Related Methods John M. Abowd Cornell University and Census Bureau January."— Presentation transcript:

1 Confidentiality Protection of Social Science Micro Data: Synthetic Data and Related Methods John M. Abowd Cornell University and Census Bureau January 30, 2006 UCLA Institute for Digital Research and Education Presentation

2 Acknowledgements Many current and past LEHD staff and senior research fellows contributed to the development of the LEHD infrastructure system and the Quarterly Workforce Indicators. Kevin McKinney, Bryce Stephens and Lars Vilhuber were particularly responsible for the confidentiality protection system. Fredrik Andersson and Marc Roemer at LEHD did the data analysis and implementation of the On the Map package. John Carpenter of Excensus, Inc. developed the mapping application. Gary Benedetto, Lisa Dragoset, Martha Stinson and Bryan Ricchetti did the synthesis programming for the SIPP-PUF application.

3 Overview What is the problem? What are synthetic data? How can the research community benefit from synthetic data? The NSF-ITR synthetic data grant The Census Bureau’s synthetic data and related products: –QWI Online –On the Map –The new SIPP-SSA-IRS Public Use File Tools

4 Information Release and Data Protection are Competing Objectives Statisticians call this the Risk-Utility tradeoff Economists prefer to distinguish between technological trade-offs and preference trade-offs Information release and data protection are technological tradeoffs

5 A Simple Example of the Technological Trade-off There are two outputs: information released and data protection Consider a census with sampling as the release technology The PPF measures the amount of information that must be sacrificed to get additional protection The information measure is Shannon’s H (or the Kullback-Liebler difference between the census and the sample) The protection measure is the maximum probability of an exact disclosure

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8 What Are Synthetic Data? Public use micro data products that reproduce essential features of confidential micro data products Essential features include: –Univariate distributions overall and in subpopulations –Multivariate relations among the variables

9 Some History Original fully synthetic data idea was due to Rubin (JOS, 1993) –Synthesize the Decennial Census long form responses for the short form households, then release samples that do not include any actual long form records Original partially synthetic data idea was due to Little (JOS, 1993) –Synthesize the sensitive values on the public use file Critical refinement (Fienberg, 1994) –Use a parametric posterior predictive distribution (instead of a Bayes bootstrap) to do the sampling Other authors, particularly Raghunathan, Reiter, Rubin, Abowd, Woodcock –Partially synthetic data with missing data (Reiter) –Sequential Regression Multivariate Imputation (Raghunathan, Reither, and Rubin; Abowd and Woodcock)

10 How Can You Preserve Confidentiality and Multivariate Relations? Fundamental trade-off: –better protection v. better data quality Protection results from summarizing the data with a complicated multivariate distribution, then sampling that distribution instead of the original data The synthetic data are not any respondent’s actual data But, for some techniques, it may still be possible to re-identify the source record in the confidential data New techniques address this problem

11 How Can the Research Community Benefit from Synthetic Data? Sophisticated research users must help develop the synthesizers in order to promote and improve analytic validity Many more users will have access to the information because there is a public use micro data product.

12 The Research – Synthetic Data Feedback Cycle Scientific Modeling Data Synthesis Confidentiality Protection Analytic Validity

13 The Multi-layer System Basic confidential data –Fundamental product of virtually all Census programs –Leads to the publication of public-use products (summary data, micro data, narrative data) Gold-standard confidential data –Edited, documented and archived research versions of confidential data –Used in internal Census research and at Research Data Centers

14 More Layers Partially-synthetic micro data –Preserves the record structure or sampling frame of the gold standard micro data –Replaces the data elements with synthetic values sampled from an appropriate probability model Fully-synthetic micro data –Uses only the population or record linkage structure of the gold standard micro data –Generates synthetic entities and data elements from appropriate probability models

15 The NSF Information Technologies Research Grant A program that encourages innovative, high-payoff IT research and education Our grant proposal cited the many research studies and data products created by previous NSF support for the Research Data Center network and the Longitudinal Employer-Household Dynamics Program

16 What Is It? $2.9 million 3-year grant to the RDC network (Cornell is the coordinating institution) Provides core support for scientific activities at the RDCs To develop public use, analytically valid synthetic data from many of the RDC- accessible data sets To facilitate collaboration with RDC projects that help design and test these products

17 The Quarterly Workforce Indicators QWI was the LEHD Program’s first public use data product QWI Online Detailed labor force information by sub- state geography, detailed industry, ownership class, sex and age group.

18 The Confidentiality Protection System All QWI protections are done by noise infusion of the micro-data All micro-data items are distorted at least minimal percentage up to a maximal percentage Only the distorted items are used in the production of the release product

19 Protection and Validity Principles Cells with few businesses contributing or with few individuals contributing have been distorted in the cross-section but not the time-series Bias in the cross-section is controlled and random, no analyst knows its sign More information

20 Theoretical Distribution of the QWI Distortion Factor

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22 Actual Confidentiality Protection Distortion: Employment, Beginning-of-Quarter

23 Table 8: Distribution of Error in First Order Serial Correlation

24 Graph: Distribution of Error in First Order Serial Correlation

25 Enhancements The current product has suppressions for cells too small to protect by noise infusion The enhanced product replaces these suppressions with synthetic data

26 Percentage of Data Items in County Level Release File

27 Beginning of Period Employment in NAICS Sector 62

28 Full Quarter New Hires in NAICS4 3259

29 The Census Bureau’s First Public Use Synthetic Data Application LEHD On-the-map application Shows commuting patterns at the Census Block level with characteristics of the origin and destination block groups Origin block data are synthetic –Sampled from the posterior predictive distribution of origin blocks and origin characteristics given destination block, destination block characteristics. On-the-map

30 Where people living in the selected area (Mobile’s neighboring communities of Daphne and Fairhope) work Source: “On the Map” beta application, Longitudinal Employer-Household Dynamics Program, U.S. Census Bureau September 23, 2005 DRAFT – Beta Test Document Only

31 Where people working in the selected area (downtown Mobile) live Source: “On the Map” beta application, Longitudinal Employer-Household Dynamics Program, U.S. Census Bureau September 23, 2005 DRAFT – Beta Test Document Only

32 Synthetic Data Model y ijk are the counts for residence block i, work place block j and characteristics k. Characteristics are age groups, earnings groups, industry (NAICS sector), ownership sector.

33 Complications Informative prior “shape” Prior “sample size” Work place counts must be compatible with the protection system used by Quarterly Workforce Indicators (QWI) –Dynamically consistent noise infusion

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36 Analytic Validity Assess the bias Assess the incremental variation

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39 Confidentiality Protection The reclassification index is a measure of how many workers were geographically relocated by the synthetic data.

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41 SIPP-SSA-IRS Public Use File Links IRS detailed earnings records and Social Security benefit data to public use SIPP data Basic confidential data: SIPP ( , 1996); W- 2 earnings data; SSA benefit data Gold standard: completely linked, edited version of the data with variables drawn from all of the sources Partially-synthetic data: created using the record structure of the existing SIPP panels with all data elements synthesized using Bayesian bootstrap and sequential regression multivariate imputation methods

42 Multiple Imputation Confidentiality Protection Denote confidential data by Y and disclosable data by X. Both Y and X may contain missing data, so that Y = (Y obs, Y mis ) and X = (X obs, X mis ). Assume database can be represented by joint density p(Y,X,θ).

43 Sequential Regression Multivariate Imputation Method Synthetic data values Y are draws from the posterior predictive density: In practice, use a two-step procedure: 1) draw m completed datasets using SRMI (imputes values for all missing data) 2) draw r synthetic datasets for each completed dataset from predictive density given the completed data.

44 Confidentiality Protection Protection is based on the inability of PUF users to re-identify the SIPP record upon which the PUF record is based. This prevents wholesale addition of SIPP data to the IRS and SSA data in the PUF Goal: re-identification of SIPP records from the PUF should result in true matches and false matches with equal probability

45 Disclosure Analysis Uses probabilistic record linking Each synthetic implicate is matched to the gold standard All unsynthesized variables are used as blocking variables Different matching variable sets are used

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47 Testing Analytic Validity Run analyses on each synthetic implicate. –Average coefficients –Combine standard errors using formulae that take account of average variance of estimates (within implicate variance) and differences in variance across estimates (between implicate variance). Run analyses on gold standard data. Compare average synthetic coefficient and standard error to the same quantities for the gold standard. Analytic validity is measured by the overlap in the coverage of the synthetic and gold standard confidence intervals for a parameter.

48 Log Annual Earnings Amount

49 Log Annual Benefit Amount

50 Tools NSF sponsored supercomputer Virtual RDC Cornell INFO 747

51 The NSF-sponsored Supercomputer on the RDC Network NSF01 is a 64-processor (384GB memory) supercomputer Installed and optimized for complex data synthesizing and simulation Projects related to the ITR grant have access and priority

52 The Virtual RDC Virtual RDC (news server)Virtual RDC The virtual RDC environment contains multiple servers that closely approximate an RDC compute server (e.g., NSF01) Disclosure-proofed metadata and synthetic data Now fully operational Any current or potential RDC user can have an account

53 Cornell Information Science 747 INFO 747 Course available to any potential RDC user, on DVD and via internet feed Training for using RDC-based data products Training for creating and testing synthetic data

54 Conclusions An important and challenging area that social scientists must be part of Use of confidential data collected by a public agency carries with it an obligation to disseminate enough data to permit scientific discourse Synthetic data is an important tool for this dissemination


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