Managing Confidentiality and Provenance 

across Mixed Private and Publicly-Accessed 

Data and Metadata 

Lars Vilhuber1 John M. Abowd1 William Block2 

Carl Lagoze3 Jeremy Williams2 

1Labor Dynamics Institute, ILR, 

2Cornell Institute for Social and Economic Research, 

3University of Michigan 

November 2013, FCSM 2013 

Vilhuber, Abowd, Block, Lagoze, Williams Data Management of Confidential Data 
Introduction Motivation Problem Solution Additional info 

Introduction 

NCRN 

I This work is part of the NSF Census Research Network 

(NCRN) - Cornell Node (”Integrated Research Support, 

Training and Data Documentation”) 

I Funded by NSF Grant #1131848. 

I For more information, see www.ncrn.cornell.edu. 

Vilhuber, Abowd, Block, Lagoze, Williams Data Management of Confidential Data 
Introduction Motivation Problem Solution Additional info 

Introduction 

Overview of work 

I Basic program outlined in Abowd, Vilhuber, and Block 

(PSD 2012) [3] and Lagoze, Block, Williams, Abowd, and 

Vilhuber, (IDCC 2013) [8] 

I PROV extension described in more detail in Lagoze, 

Williams, Vilhuber (Metadata and Semantics Research 

Conference, November 2013) and Lagoze et al (European 

DDI User Confernce, December 2013) [9] 

Vilhuber, Abowd, Block, Lagoze, Williams Data Management of Confidential Data 
Introduction Motivation Problem Solution Additional info 

Introduction 

Some facts that motivated us 

Stating the problem in the U.S. case 

CED2AR: A proposed solution 

What is DDI 

DDI extension for confidentiality protection 

DDI extension for provenance tracing 

Vilhuber, Abowd, Block, Lagoze, Williams Data Management of Confidential Data 
Introduction Motivation Problem Solution Additional info 

Replication of research results 

Critical element of science 

I Replication of methods, data inputs, computational 

environment is a critical element of the scientific approach 

I Journals, funding agencies (in the U.S.) have been moving 

to making archiving of inputs to scientific results more 

robust, even mandatory 

Vilhuber, Abowd, Block, Lagoze, Williams Data Management of Confidential Data 
Introduction Motivation Problem Solution Additional info 

Not a new problem 

Econometrica 

“In its first issue, the editor of Econometrica (1933), Ragnar 

Frisch, noted the importance of publishing data such that 

readers could fully explore empirical results. Publication of 

data, however, was discontinued early in the journal’s history. 

[...] The journal arrived full-circle in late 2004 when 

Econometrica adopted one of the more stringent policies on 

availability of data and programs. 

http://www.econometricsociety.org/submissions.asp#4 as cited in Anderson et al (2005) 

Vilhuber, Abowd, Block, Lagoze, Williams Data Management of Confidential Data 
Introduction Motivation Problem Solution Additional info 

Problem will become worse 

Increased use of restricted-access data 

I Today’s young scholars pursue research programs that 

mandate inherently identifiable data 

I Geospatial relations, 

I Exact genome data, 

I Networks of all sorts, 

I Linked administrative records 

I These researchers acquire authorized, generally 

unfettered, restricted access to the confidential, identifiable 

data and perform their analyses in secure environments. 

I Archiving (curation) of input data is complicated 

I Knowledge discovery is complicated 

Vilhuber, Abowd, Block, Lagoze, Williams Data Management of Confidential Data 
Introduction Motivation Problem Solution Additional info 

Decline in the use of classic public-use data 

Vilhuber, Abowd, Block, Lagoze, Williams Data Management of Confidential Data 
Introduction Motivation Problem Solution Additional info 

Increase in the use of administrative data in 

economics 

Vilhuber, Abowd, Block, Lagoze, Williams Data Management of Confidential Data 
Introduction Motivation Problem Solution Additional info 

Not limited to economics 

Nature, 2012 

“Many of the emerging ‘big data’ applications come from private 

sources that are inaccessible to other researchers. The data 

source may be hidden, compounding problems of verification, 

as well as concerns about the generality of the results.” 

(Huberman, Nature 482, 308 (16 February 2012) doi:10.1038/482308d) 

Other domains 

I Biology (genetics data, chemical compounds) 

I Computer science (search records, single-firm examples) 

Vilhuber, Abowd, Block, Lagoze, Williams Data Management of Confidential Data 
Introduction Motivation Problem Solution Additional info 

Introduction 

Some facts that motivated us 

Stating the problem in the U.S. case 

CED2AR: A proposed solution 

What is DDI 

DDI extension for confidentiality protection 

DDI extension for provenance tracing 

Vilhuber, Abowd, Block, Lagoze, Williams Data Management of Confidential Data 
Introduction Motivation Problem Solution Additional info 

Why we think there is a problem 

Core issues 

a Insufficient curation (starting with archiving) 

b No way to reference data (unique identifiers) 

c No consistent way to learn about the data (metadata 

dissemination) 

d Weak or non-existent provenance tracing 

Vilhuber, Abowd, Block, Lagoze, Williams Data Management of Confidential Data 
Introduction Motivation Problem Solution Additional info 

Generalized problem 

Multiple data sources in the US 

I U.S. Census Bureau (RDC) more

I more Internal Revenue Service (confidential, public-use) 

I Bureau of Labor Statistics (confidential, public-use 

data) more 

Present elsewhere? 

I Canada: 

I Centre for Data Development and Economic Research 

(CDER: RDC-like for business data) more 

I better: Canadian RDC network more

I France: Reseau Quetelet , Centre d’acc  ́ more es s  ́ ecuris  ́ e  ́

distant aux donn ees (CASD)  ́

I Germany: IAB 

Vilhuber, Abowd, Block, Lagoze, Williams Data Management of Confidential Data 
Introduction Motivation Problem Solution Additional info 

Introduction 

Some facts that motivated us 

Stating the problem in the U.S. case 

CED2AR: A proposed solution 

What is DDI 

DDI extension for confidentiality protection 

DDI extension for provenance tracing 

Vilhuber, Abowd, Block, Lagoze, Williams Data Management of Confidential Data 
Introduction Motivation Problem Solution Additional info 

Comprehensive Extensible Data Documentation and 

Access (CED2AR) 

Core 

We develop the core of a method for solving the data archive 

and curation problem that confronts the custodians of 

restricted-access research data and the scientific users of such 

data. Our solution recognizes the dual protections afforded by 

physical security and access limitation protocols, and allows for 

much improved provenance tracing. 

Vilhuber, Abowd, Block, Lagoze, Williams Data Management of Confidential Data 
Introduction Motivation Problem Solution Additional info 

Proposed solution 

Extensible framework 

I Based on existing standards (Data Documentation 

Initiative, DDI) with extension to accomodate disclosure 

protection mechanisms and provenance tracing 

Vilhuber, Abowd, Block, Lagoze, Williams Data Management of Confidential Data 
Introduction Motivation Problem Solution Additional info 

Proposed solution 

Extensible framework 

I Based on existing standards (Data Documentation 

Initiative, DDI) with extension to accomodate disclosure 

protection mechanisms and provenance tracing 

I Connectors (import/export) to other sources and standards 

Vilhuber, Abowd, Block, Lagoze, Williams Data Management of Confidential Data 
Introduction Motivation Problem Solution Additional info 

Proposed solution 

Extensible framework 

I Based on existing standards (Data Documentation 

Initiative, DDI) with extension to accomodate disclosure 

protection mechanisms and provenance tracing 

I Connectors (import/export) to other sources and standards 

I To be filled by multiple sources of metadata (some the 

curators/owners, others “crowd-sourced”) 

Vilhuber, Abowd, Block, Lagoze, Williams Data Management of Confidential Data 
Introduction Motivation Problem Solution Additional info 

Proposed solution 

Extensible framework 

I Based on existing standards (Data Documentation 

Initiative, DDI) with extension to accomodate disclosure 

protection mechanisms and provenance tracing 

I Connectors (import/export) to other sources and standards 

I To be filled by multiple sources of metadata (some the 

curators/owners, others “crowd-sourced”) 

I Interim solution for those datasets without unique 

identifiers (Digital Object Identifier, DOI) 

Vilhuber, Abowd, Block, Lagoze, Williams Data Management of Confidential Data 
Introduction Motivation Problem Solution Additional info 

Proposed solution 

Extensible framework 

I Based on existing standards (Data Documentation 

Initiative, DDI) with extension to accomodate disclosure 

protection mechanisms and provenance tracing 

I Connectors (import/export) to other sources and standards 

I To be filled by multiple sources of metadata (some the 

curators/owners, others “crowd-sourced”) 

I Interim solution for those datasets without unique 

identifiers (Digital Object Identifier, DOI) 

Vilhuber, Abowd, Block, Lagoze, Williams Data Management of Confidential Data 
Introduction Motivation Problem Solution Additional info 

What is DDI? 

Vilhuber, Abowd, Block, Lagoze, Williams Data Management of Confidential Data 
Introduction Motivation Problem Solution Additional info 

Example of DDI 

<?xml version="1.0" encoding="UTF-8"?>

<codeBook xmlns="ddi:codebook:2_5" ...>

<docDscr>

<citation>

<titlStmt>

<titl>SIPP_Synthetic_Beta</titl>

<altTitl>SSB</altTitl>

<IDNo agency="DOI">TBD</IDNo>

</titlStmt>

<rspStmt>

<AuthEnty affiliation="Cornell University">

Virtual RDC

</AuthEnty>

</rspStmt>

Vilhuber, Abowd, Block, Lagoze, Williams Data Management of Confidential Data 
Introduction Motivation Problem Solution Additional info 

..better seen as 

Vilhuber, Abowd, Block, Lagoze, Williams Data Management of Confidential Data 
Introduction Motivation Problem Solution Additional info 

Example DDI: ICPSR 

Vilhuber, Abowd, Block, Lagoze, Williams Data Management of Confidential Data 
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Example DDI: UK data archive 

Vilhuber, Abowd, Block, Lagoze, Williams Data Management of Confidential Data 
Introduction Motivation Problem Solution Additional info 

Expanded DDI attributes 

Standard DDI 

Fragment of variable description∗

<var ID="V1" dcml="0" files="F1" intrvl="discrete"

name="cur_end mar_flag">

<location width="12"/>

<labl>Flag: Linked marriage ended</labl>

<valrng>

<range UNITS="REAL" max="2" min="0"/>

</valrng>

<sumStat type="vald"> 123 </sumStat>

<sumStat type="invd"> 456 </sumStat>

<catgry>

<catValu> 1 </catValu>

<catStat type="freq"> 234 </catStat>

</catgry>

∗ All values are fake 

Vilhuber, Abowd, Block, Lagoze, Williams Data Management of Confidential Data 
Introduction Motivation Problem Solution Additional info 

Expanded DDI attributes 

Standard DDI 

Fragment of variable description∗

<!--var ID="V1" dcml="0" files="F1" intrvl="discrete"

name="cur_end mar_flag">

<location width="12"/>

<labl>Flag: Linked marriage ended</labl -->

<valrng>

<range UNITS="REAL" max="2" min="0"/>

</valrng>

<!-- sumStat type="vald"> 123 </sumStat>

<sumStat type="invd"> 456 </sumStat>

<catgry>

<catValu> 1 </catValu>

<catStat type="freq"> 234 </catStat>

</catgry -->

∗ All values are fake 

Vilhuber, Abowd, Block, Lagoze, Williams Data Management of Confidential Data 
Introduction Motivation Problem Solution Additional info 

Expanded DDI attributes 

Standard DDI 

Fragment of variable description∗

<!--var ID="V1" dcml="0" files="F1" intrvl="discrete"

name="cur_end mar_flag">

<location width="12"/>

<labl>Flag: Linked marriage ended</labl>

<valrng>

<range UNITS="REAL" max="2" min="0"/>

</valrng -->

<sumStat type="vald"> 123 </sumStat>

<sumStat type="invd"> 456 </sumStat>

<!-- catgry>

<catValu> 1 </catValu>

<catStat type="freq"> 234 </catStat>

</catgry -->

∗ All values are fake 

Vilhuber, Abowd, Block, Lagoze, Williams Data Management of Confidential Data 
Introduction Motivation Problem Solution Additional info 

Expanded DDI attributes 

Enhanced DDI 

Re-using existing attribute, but expanding scope.∗

<var ID="V1" dcml="0" files="F1" intrvl="discrete"

name="cur_end mar_flag">

<location width="12"/>

<labl>Flag: Linked marriage ended</labl>

<valrng access="release">

<range UNITS="REAL" max="2" min="0"/>

</valrng>

<sumStat access="restricted" type="vald"> 123

<sumStat access="restricted" type="invd"> 456

<catgry access="release">

<catValu access="release"> 1 </catValu>

<catStat type="freq" access="restricted">

234

</catStat>

</catgry>

∗ All values are fake 

Vilhuber, Abowd, Block, Lagoze, Williams 

</sumS

</sumS

Data Management of Confidential Data 
Introduction Motivation Problem Solution Additional info 

Expanded DDI attributes 

Enhanced DDI 

Allows for verifiable filtering∗

<var ID="V1" dcml="0" files="F1" intrvl="discrete"

name="cur_end mar_flag">

<location width="12"/>

<labl>Flag: Linked marriage ended</labl>

<valrng access="release">

<range UNITS="REAL" max="2" min="0"/>

</valrng>

<!-- sumStat suppressed -->

<!-- sumStat suppressed -->

<catgry access="release">

<catValu access="release"> 1 </catValu>

<catStat type="freq" access="restricted">

[suppressed]

</catStat>

</catgry>

∗ All values are fake 

Vilhuber, Abowd, Block, Lagoze, Williams Data Management of Confidential Data 
Introduction Motivation Problem Solution Additional info 

Application to confidentiality protection 

Vilhuber, Abowd, Block, Lagoze, Williams Data Management of Confidential Data 
Introduction Motivation Problem Solution Additional info 

Options 

I Variable is suppressed, including all subordinate elements 

I Variable description is released, but all subordinate 

statistical elements are suppressed (attribute of < var >

set to ”released”) [default] 

I Expand all existing attributes, individually select 

subordinate elements to suppress (attribute of sub-element 

is set to ”suppressed”, content suppressed) 

Vilhuber, Abowd, Block, Lagoze, Williams Data Management of Confidential Data 
Introduction Motivation Problem Solution Additional info 

Application to confidentiality protection 

Vilhuber, Abowd, Block, Lagoze, Williams Data Management of Confidential Data 
Introduction Motivation Problem Solution Additional info 

Application to confidentiality protection 

Vilhuber, Abowd, Block, Lagoze, Williams Data Management of Confidential Data 
Introduction Motivation Problem Solution Additional info 

Implementation 

Definitions 

I First draft of specification in test use by our team 

Vilhuber, Abowd, Block, Lagoze, Williams Data Management of Confidential Data 
Introduction Motivation Problem Solution Additional info 

Implementation 

Definitions 

I First draft of specification in test use by our team 

I Full enhanced specification (based on DDI-Codebook 2.5) 

published on CED2AR 

Vilhuber, Abowd, Block, Lagoze, Williams Data Management of Confidential Data 
Introduction Motivation Problem Solution Additional info 

Implementation 

Definitions 

I First draft of specification in test use by our team 

I Full enhanced specification (based on DDI-Codebook 2.5) 

published on CED2AR 

I Enhanced specification proposed to DDI Alliance 

Vilhuber, Abowd, Block, Lagoze, Williams Data Management of Confidential Data 
Introduction Motivation Problem Solution Additional info 

Implementation 

Definitions 

I First draft of specification in test use by our team 

I Full enhanced specification (based on DDI-Codebook 2.5) 

published on CED2AR 

I Enhanced specification proposed to DDI Alliance 

I Expand to DDI-Lifecycle 

Vilhuber, Abowd, Block, Lagoze, Williams Data Management of Confidential Data 
Introduction Motivation Problem Solution Additional info 

Provenance 

The provenance problem 

“data provenance, one kind of metadata, pertains to the 

derivation history of a data product starting from its original 

sources” [...] “from it, one can ascertain the quality of the data 

base and its ancestral data and derivations, track back sources 

of errors, allow automated reenactment of derivations to update 

the data, and provide attribution of data sources” 

Simmhan, Plale, and Gannon, “A survey of data provenance in e-science,” ACM Sigmod Record, 2005 

Vilhuber, Abowd, Block, Lagoze, Williams Data Management of Confidential Data 
Introduction Motivation Problem Solution Additional info 

Support in DDI 

Provenance and Metadata 

Not (currently) a “native” component of DDI, closest thing is: 

<xs:complexType name="othrStdyMatType">

<xs:complexContent>

<xs:extension base="baseElementType">

<xs:sequence>

<xs:element ref="relMat" minOccurs="0" maxOccurs="unbounded"/>

<xs:element ref="relStdy" minOccurs="0" maxOccurs="unbounded"/>

<xs:element ref="relPubl" minOccurs="0" maxOccurs="unbounded"/>

<xs:element ref="othRefs" minOccurs="0" maxOccurs="unbounded"/>

</xs:sequence>

</xs:extension>

</xs:complexContent>

</xs:complexType>

Downside 

No structure. Mostly verbose entries. 

Vilhuber, Abowd, Block, Lagoze, Williams Data Management of Confidential Data 
Introduction Motivation Problem Solution Additional info 

Only a verbose description 

Vilhuber, Abowd, Block, Lagoze, Williams Data Management of Confidential Data 
Introduction Motivation Problem Solution Additional info 

UK Data Archive example 

Vilhuber, Abowd, Block, Lagoze, Williams Data Management of Confidential Data 
Introduction Motivation Problem Solution Additional info 

Provenance (cont) 

PROV model 

W3C PROV Model based in the notions of 

1. entities that are physical, digital, and conceptual things in 

the world; 

2. activities that are dynamic aspects of the world that 

change and create entities; and 

3. agents that are responsible for activities. 

4. a set of relationships that can exist be- tween them that 

express attribution,. delegation, derivation, etc. 

Vilhuber, Abowd, Block, Lagoze, Williams Data Management of Confidential Data 
Introduction Motivation Problem Solution Additional info 

Incorporating PROV (LBD) 

Vilhuber, Abowd, Block, Lagoze, Williams Data Management of Confidential Data 
Introduction Motivation Problem Solution Additional info 

Incorporating PROV (LBD) 

Vilhuber, Abowd, Block, Lagoze, Williams Data Management of Confidential Data 
Introduction Motivation Problem Solution Additional info 

Incorporating PROV (LBD) 

Vilhuber, Abowd, Block, Lagoze, Williams Data Management of Confidential Data 
Introduction Motivation Problem Solution Additional info 

PROV as RDF 

Vilhuber, Abowd, Block, Lagoze, Williams Data Management of Confidential Data 
Introduction Motivation Problem Solution Additional info 

The key PROV element embedded as DDI/XML 

<stdyDscr> <!-- Standard DDI 2.5 -->

<othrStdyMat> <!-- Standard DDI 2.5 -->

<relStdy> <!-- Standard DDI 2.5 -->

<!-- From here, PROV additions -->

<prov:wasDerivedFrom>

<prov:generatedEntity prov:ref="cdr:LBD"/>

<prov:usedEntity prov:ref="cdr:BR"/>

</prov:wasDerivedFrom>

<prov:wasAssociatedWith>

<prov:activity prov:ref="cdr:procLBD"/>

<prov:agent prov:ref="cdr:CES"/>

<prov:plan prov:ref="cdr:procLBDPlan"/>

</prov:wasAssociatedWith>

</relStdy> <!-- Standard DDI 2.5 -->

</othrStdyMat> <!-- Standard DDI 2.5 -->

</stdyDscr><!-- Standard DDI 2.5 -->

Vilhuber, Abowd, Block, Lagoze, Williams Data Management of Confidential Data 
Introduction Motivation Problem Solution Additional info 

Additional PROV elements 

These could be derived from existing DDI elements (still being 

developed) 

<!-- Entities -->

<prov:entity prov:id="cdr:BR">

<dct:title>Business Register</dct:title>

</prov:entity>

<!-- Plans = Methodology -->

<prov:plan prov:id="cdr:procLBDPlan">

<prov:location

xsi:type="xsd:anyURI">

http://ideas.repec.org/p/cen/wpaper/02-17.html

</prov:location>

<prov:type>prov:Plan</prov:type>

</prov:plan>

Vilhuber, Abowd, Block, Lagoze, Williams Data Management of Confidential Data 
Introduction Motivation Problem Solution Additional info 

Work on PROV 

More details forthcoming 

I Lagoze, Williams, Vilhuber “Encoding Provenance 

Metadata for Social Science Datasets”, submitted to 

Metadata and Semantics Research Conference 

(November 2013) 

I Lagoze, Williams, Vilhuber, Block “Encoding Provenance 

of Social Science Data: Integrating PROV with DDI”, 

accepted for 5th Annual European DDI User Conference 

(December 2013) 

Vilhuber, Abowd, Block, Lagoze, Williams Data Management of Confidential Data 
Introduction Motivation Problem Solution Additional info 

Usage scenario 

Vilhuber, Abowd, Block, Lagoze, Williams Data Management of Confidential Data 
Introduction Motivation Problem Solution Additional info 

Usage scenario 

Vilhuber, Abowd, Block, Lagoze, Williams Data Management of Confidential Data 
Introduction Motivation Problem Solution Additional info 

Usage scenario 

Vilhuber, Abowd, Block, Lagoze, Williams Data Management of Confidential Data 
Introduction Motivation Problem Solution Additional info 

Usage scenario 

Vilhuber, Abowd, Block, Lagoze, Williams Data Management of Confidential Data 
Introduction Motivation Problem Solution Additional info 

Highlighting provenance 

Vilhuber, Abowd, Block, Lagoze, Williams Data Management of Confidential Data 
Introduction Motivation Problem Solution Additional info 

CED2AR next steps 

I Formalize the DDI extension 

Vilhuber, Abowd, Block, Lagoze, Williams Data Management of Confidential Data 
Introduction Motivation Problem Solution Additional info 

CED2AR next steps 

I Formalize the DDI extension 

I Provide implementation outside of Census Bureau 

Vilhuber, Abowd, Block, Lagoze, Williams Data Management of Confidential Data 
Introduction Motivation Problem Solution Additional info 

CED2AR next steps 

I Formalize the DDI extension 

I Provide implementation outside of Census Bureau 

I Test implementation within the Census RDC 

Vilhuber, Abowd, Block, Lagoze, Williams Data Management of Confidential Data 
Introduction Motivation Problem Solution Additional info 

The end 

Thank you 

I [3] for more details 

I Labor Dynamics Institute 

I VirtualRDC @ Cornell 

I NCRN Cornell website 

Vilhuber, Abowd, Block, Lagoze, Williams Data Management of Confidential Data 
Introduction Motivation Problem Solution Additional info 

$Id: Presentation-FCSM2013-subdoc.tex 405

Vilhuber, Abowd, Block, Lagoze, Williams 

2013-11-0

Data Management of Confidential Data 
Extra slides 

Extra slides 

Census Bureau 

IRS 

BLS 

CDER 

CRDC 

France 

Vilhuber, Abowd, Block, Lagoze, Williams Data Management of Confidential Data 
Extra slides 

Dataset usage in Census RDC 

1,505 project-dataset pairs 

Many projects use multiple datasets. 

Vilhuber, Abowd, Block, Lagoze, Williams Data Management of Confidential Data 
Extra slides 

Economic (business) datasets 

I 71% of datasets are business (economic) datasets 

I Primarily establishment-based records from the Economic 

Censuses and Surveys, the Business Register, and the 

Longitudinal Business Database (LBD) 

I They form the core of the modern industrial organization 

studies [5, 11] as well as modern gross job creation and 

destruction in macroeconomics [4, 6]. 

I But there are no public-use micro-data for these 

establishment-based products 

I Exception: recently-released Synthetic LBD [2, 7] 

I Currently no active curation (of derived datasets) [a], no 

way to reference [b], convoluted way to learn about the 

data structure [c∗

] 

Vilhuber, Abowd, Block, Lagoze, Williams Data Management of Confidential Data 
Extra slides 

LEHD data 

Linked employer-employee data 

I Longitudinal and cross-sectional detail 

I New confidentiality protection methodologies [1, 10] have 

unlocked large amounts of data for public-use: highly 

detailed local area tabulations exist based on the LEHD 

data 

I But: no public-use micro-data exist for this longitudinal job 

frame or any of its derivative files. 

I Confidential data are dynamic (quarterly changes) 

I Currently some active curation (archiving, 10-yr!) [a∗

], no 

way to reference (publicly) [b∗

], convoluted way to learn 

about the data structure [c∗

] 

Vilhuber, Abowd, Block, Lagoze, Williams Data Management of Confidential Data 
Extra slides 

Not unique to Census Bureau 

Internal Revenue Service/ Social Security Administration 

I New projects (Chetty et al, 2012; von Wachter and 

co-authors) have created and/or used linked longitudinal 

data at the IRS or the Social Security Administration. 

I Neither agency has long-run experience at the statistical 

data curation function [a], (meta)data dissemination [b,c]. 

I Although both IRS and SSA have produced statistical 

tables for a long time. 

Vilhuber, Abowd, Block, Lagoze, Williams Data Management of Confidential Data 
Extra slides 

Not unique to Census Bureau 

Bureau of Labor Statistics 

I Long history of making time-series available 

I Limited access to microdata at the BLS 

I Unknown curation [a] 

I Even for public-use data, no way to reference specific 

releases [b] 

I No well-established way to learn about microdata [c] 

Vilhuber, Abowd, Block, Lagoze, Williams Data Management of Confidential Data 
Extra slides 

Canadian Centre for Data Development and Economic 

Research 

Vilhuber, Abowd, Block, Lagoze, Williams Data Management of Confidential Data 
Extra slides 

Canadian Research Data Centres 

Vilhuber, Abowd, Block, Lagoze, Williams Data Management of Confidential Data 
Extra slides 

Canadian Research Data Centres 

... but also not perfect 

Attempt to access data information on General Social Survey 

Vilhuber, Abowd, Block, Lagoze, Williams Data Management of Confidential Data 
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Reseau Quetelet  ́

Vilhuber, Abowd, Block, Lagoze, Williams Data Management of Confidential Data 
Extra slides 

J. M. Abowd, K. Gittings, K. L. McKinney, B. E. Stephens, L. Vilhuber, and S. Woodcock, “Dynamically 

consistent noise infusion and partially synthetic data as confidentiality protection measures for related 

time-series,” Federal Committee on Statistical Methodology, Tech. Rep., January 2012. [Online]. Available: 

http://www.fcsm.gov/events/papers2012.html 

J. M. Abowd and L. Vilhuber. (2010) Synthetic data server. [Online]. Available: 

http://www.vrdc.cor nell.edu/sds/ 

J. M. Abowd, L. Vilhuber, and W. Block, “A proposed solution to the archiving and curation of confidential 

scientific inputs,” in Privacy in Statistical Databases, ser. Lecture Notes in Computer Science, 

J. Domingo-Ferrer and I. Tinnirello, Eds., vol. 7556. Springer, 2012, pp. 216–225. [Online]. Available: 

http://dx.doi.org/10.1007/978-3-642-33627-0 17 

S. J. Davis, J. C. Haltiwanger, and S. Schuh, Job creation and destruction. Cambridge, MA: MIT Press, 

1996. 

T. Dunne, M. J. Roberts, and L. Samuelson, “The Growth and Failure of U.S. Manufacturing Plants,” 

Quarterly Journal of Economics, vol. 104, no. 4, pp. 671–698, 1989. 

J. Haltiwanger, R. S. Jarmin, and J. Miranda, “Who creates jobs? Small vs. large vs. young,” Center for 

Economic Studies, U.S. Census Bureau, Working Papers 10-17, Aug. 2010. [Online]. Available: 

http://ideas.repec.org/p/cen/wpaper/10-17.html 

S. K. Kinney, J. P. Reiter, A. P. Reznek, J. Miranda, R. S. Jarmin, and J. M. Abowd, “Towards unrestricted 

public use business microdata: The synthetic longitudinal business database,” International Statistical

Review, vol. 79, no. 3, pp. 362–384, December 2011. [Online]. Available: 

http://ideas.repec.org/a/bla/istatr/v79y2011i3p362-384.html 

C. Lagoze, W. C. Block, J. Williams, J. M. Abowd, and L. Vilhuber, “Data management of confidential data,” 

International Journal of Digital Curation, vol. 8, no. 1, pp. 265–278, 2013, presented at 8th International 

Digital Curation Conference 2013, Amsterdam. See also http://hdl.handle.net/1813/30924. 

C. Lagoze, W. C. Block, J. Williams, and L. Vilhuber, “Encoding provenance of social science data: 

Integrating prov with ddi,” in 5th Annual European DDI User Conference

A. Machanavajjhala, D. Kifer, J. M. Abowd, J. Gehrke, and L. Vilhuber, “Privacy: Theory meets practice on Data Management of Confidential Data 

, accepted. 

Vilhuber, Abowd, Block, Lagoze, Williams