There is a newer version of this record available.

Working paper Open Access

Introductory Readings in Formal Privacy for Economists

John Abowd; Ian Schmutte; William Sexton; Lars Vilhuber


MARC21 XML Export

<?xml version='1.0' encoding='UTF-8'?>
<record xmlns="http://www.loc.gov/MARC21/slim">
  <leader>00000nam##2200000uu#4500</leader>
  <datafield tag="041" ind1=" " ind2=" ">
    <subfield code="a">eng</subfield>
  </datafield>
  <datafield tag="653" ind1=" " ind2=" ">
    <subfield code="a">Privacy</subfield>
  </datafield>
  <datafield tag="653" ind1=" " ind2=" ">
    <subfield code="a">Official Statistics</subfield>
  </datafield>
  <datafield tag="653" ind1=" " ind2=" ">
    <subfield code="a">Differential Privacy</subfield>
  </datafield>
  <datafield tag="653" ind1=" " ind2=" ">
    <subfield code="a">Economics</subfield>
  </datafield>
  <datafield tag="653" ind1=" " ind2=" ">
    <subfield code="a">Economics of Privacy</subfield>
  </datafield>
  <datafield tag="653" ind1=" " ind2=" ">
    <subfield code="a">Statistical Disclosure Limitation</subfield>
  </datafield>
  <controlfield tag="005">20190508193422.0</controlfield>
  <datafield tag="500" ind1=" " ind2=" ">
    <subfield code="a">Supported by Alfred P. Sloan Foundation Grant G-2015-13903 and NSF Grant SES-1131848</subfield>
  </datafield>
  <controlfield tag="001">2621345</controlfield>
  <datafield tag="700" ind1=" " ind2=" ">
    <subfield code="u">University of Georgia</subfield>
    <subfield code="0">(orcid)0000-0001-5955-3599</subfield>
    <subfield code="a">Ian Schmutte</subfield>
  </datafield>
  <datafield tag="700" ind1=" " ind2=" ">
    <subfield code="u">U.S. Census Bureau</subfield>
    <subfield code="a">William Sexton</subfield>
  </datafield>
  <datafield tag="700" ind1=" " ind2=" ">
    <subfield code="u">Cornell University</subfield>
    <subfield code="0">(orcid)0000-0001-5733-8932</subfield>
    <subfield code="a">Lars Vilhuber</subfield>
  </datafield>
  <datafield tag="856" ind1="4" ind2=" ">
    <subfield code="s">201806</subfield>
    <subfield code="z">md5:7008fc407f69c1b71b9f0cf6852ccbb8</subfield>
    <subfield code="u">https://zenodo.org/record/2621345/files/labordynamicsinstitute/privacy-bibliography-v20190402.zip</subfield>
  </datafield>
  <datafield tag="542" ind1=" " ind2=" ">
    <subfield code="l">open</subfield>
  </datafield>
  <datafield tag="260" ind1=" " ind2=" ">
    <subfield code="c">2019-04-02</subfield>
  </datafield>
  <datafield tag="909" ind1="C" ind2="O">
    <subfield code="p">openaire</subfield>
    <subfield code="o">oai:zenodo.org:2621345</subfield>
  </datafield>
  <datafield tag="100" ind1=" " ind2=" ">
    <subfield code="u">U.S. Census Bureau</subfield>
    <subfield code="0">(orcid)0000-0002-0998-4531</subfield>
    <subfield code="a">John Abowd</subfield>
  </datafield>
  <datafield tag="245" ind1=" " ind2=" ">
    <subfield code="a">Introductory Readings in Formal Privacy for Economists</subfield>
  </datafield>
  <datafield tag="536" ind1=" " ind2=" ">
    <subfield code="c">1131848</subfield>
    <subfield code="a">NCRN-MN: Cornell Census-NSF Research Node: Integrated Research Support, Training and Data Documentation</subfield>
  </datafield>
  <datafield tag="540" ind1=" " ind2=" ">
    <subfield code="u">http://creativecommons.org/licenses/by-nc/4.0/legalcode</subfield>
    <subfield code="a">Creative Commons Attribution Non Commercial 4.0 International</subfield>
  </datafield>
  <datafield tag="650" ind1="1" ind2="7">
    <subfield code="a">cc-by</subfield>
    <subfield code="2">opendefinition.org</subfield>
  </datafield>
  <datafield tag="520" ind1=" " ind2=" ">
    <subfield code="a">&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;</subfield>
  </datafield>
  <datafield tag="773" ind1=" " ind2=" ">
    <subfield code="n">url</subfield>
    <subfield code="i">isSupplementTo</subfield>
    <subfield code="a">https://github.com/labordynamicsinstitute/privacy-bibliography/tree/v20190402</subfield>
  </datafield>
  <datafield tag="773" ind1=" " ind2=" ">
    <subfield code="n">doi</subfield>
    <subfield code="i">isVersionOf</subfield>
    <subfield code="a">10.5281/zenodo.2621344</subfield>
  </datafield>
  <datafield tag="024" ind1=" " ind2=" ">
    <subfield code="a">10.5281/zenodo.2621345</subfield>
    <subfield code="2">doi</subfield>
  </datafield>
  <datafield tag="980" ind1=" " ind2=" ">
    <subfield code="a">publication</subfield>
    <subfield code="b">workingpaper</subfield>
  </datafield>
</record>
79
5
views
downloads
All versions This version
Views 7957
Downloads 53
Data volume 2.4 MB605.4 kB
Unique views 6049
Unique downloads 42

Share

Cite as