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Introductory Readings in Formal Privacy for Economists

John Abowd; Ian Schmutte; William Sexton; Lars Vilhuber


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  <identifier identifierType="DOI">10.5281/zenodo.2621345</identifier>
  <creators>
    <creator>
      <creatorName>John Abowd</creatorName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0002-0998-4531</nameIdentifier>
      <affiliation>U.S. Census Bureau</affiliation>
    </creator>
    <creator>
      <creatorName>Ian Schmutte</creatorName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0001-5955-3599</nameIdentifier>
      <affiliation>University of Georgia</affiliation>
    </creator>
    <creator>
      <creatorName>William Sexton</creatorName>
      <affiliation>U.S. Census Bureau</affiliation>
    </creator>
    <creator>
      <creatorName>Lars Vilhuber</creatorName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0001-5733-8932</nameIdentifier>
      <affiliation>Cornell University</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Introductory Readings in Formal Privacy for Economists</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2019</publicationYear>
  <subjects>
    <subject>Privacy</subject>
    <subject>Official Statistics</subject>
    <subject>Differential Privacy</subject>
    <subject>Economics</subject>
    <subject>Economics of Privacy</subject>
    <subject>Statistical Disclosure Limitation</subject>
  </subjects>
  <dates>
    <date dateType="Issued">2019-04-02</date>
  </dates>
  <language>en</language>
  <resourceType resourceTypeGeneral="Text">Working paper</resourceType>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/2621345</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsSupplementTo">https://github.com/labordynamicsinstitute/privacy-bibliography/tree/v20190402</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.5281/zenodo.2621344</relatedIdentifier>
  </relatedIdentifiers>
  <version>v20190402</version>
  <rightsList>
    <rights rightsURI="http://creativecommons.org/licenses/by-nc/4.0/legalcode">Creative Commons Attribution Non Commercial 4.0 International</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;The purpose of this document is to provide scholars with a comprehensive list of readings relevant to the economic analysis of formal privacy, and particularly its application to public statistics. Statistical agencies and tech giants are rapidly adopting formal privacy models which make the tradeoff between privacy and data quality precise. The question then becomes, how much privacy loss should they allow? Abowd and Schmutte (2019) argue that this choice ultimately depends on how decision makers weigh the costs of privacy loss against the benefits of higher-quality data. Making progress on these questions requires familiarity with new tools from computer science and statistics, the objectives and policy environment within which statistical agencies operate, along with the economic analysis of information.&lt;/p&gt;

&lt;p&gt;We have organized these references into a reading course focused on 10-15 primary references in each of six different topics:&lt;/p&gt;

&lt;ul&gt;
	&lt;li&gt;Formal Privacy&lt;/li&gt;
	&lt;li&gt;Policy and Official Statistics&lt;/li&gt;
	&lt;li&gt;Statistical Disclosure Limitation&lt;/li&gt;
	&lt;li&gt;Economics of Privacy&lt;/li&gt;
	&lt;li&gt;Value of Privacy and Data Accuracy&lt;/li&gt;
&lt;/ul&gt;</description>
    <description descriptionType="Other">Supported by Alfred P. Sloan Foundation Grant G-2015-13903 and NSF Grant SES-1131848</description>
  </descriptions>
  <fundingReferences>
    <fundingReference>
      <funderName>National Science Foundation</funderName>
      <funderIdentifier funderIdentifierType="Crossref Funder ID">10.13039/100000001</funderIdentifier>
      <awardNumber awardURI="info:eu-repo/grantAgreement/NSF/Directorate+for+Social%2C+Behavioral+%26+Economic+Sciences/1131848/">1131848</awardNumber>
      <awardTitle>NCRN-MN: Cornell Census-NSF Research Node: Integrated Research Support, Training and Data Documentation</awardTitle>
    </fundingReference>
  </fundingReferences>
</resource>
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