Software Open Access

Private-PGM

McKenna, Ryan; Miklau, Gerome; Sheldon, Daniel


DataCite XML Export

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  <identifier identifierType="DOI">10.5281/zenodo.5548533</identifier>
  <creators>
    <creator>
      <creatorName>McKenna, Ryan</creatorName>
      <givenName>Ryan</givenName>
      <familyName>McKenna</familyName>
      <affiliation>College of Information and Computer Sciences, The University of Massachusets, Amherst, MA</affiliation>
    </creator>
    <creator>
      <creatorName>Miklau, Gerome</creatorName>
      <givenName>Gerome</givenName>
      <familyName>Miklau</familyName>
      <affiliation>College of Information and Computer Sciences, The University of Massachusets, Amherst, MA</affiliation>
    </creator>
    <creator>
      <creatorName>Sheldon, Daniel</creatorName>
      <givenName>Daniel</givenName>
      <familyName>Sheldon</familyName>
      <affiliation>College of Information and Computer Sciences, The University of Massachusets, Amherst, MA</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Private-PGM</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2021</publicationYear>
  <dates>
    <date dateType="Issued">2021-10-04</date>
  </dates>
  <resourceType resourceTypeGeneral="Software"/>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/5548533</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsSupplementTo">https://github.com/journalprivacyconfidentiality/private-pgm-jpc-778/tree/v2021-10-04-jpc</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsSupplementTo" resourceTypeGeneral="JournalArticle">10.29012/jpc.778</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.5281/zenodo.5548532</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://zenodo.org/communities/jpc</relatedIdentifier>
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  <version>v2021-10-04-jpc</version>
  <rightsList>
    <rights rightsURI="https://opensource.org/licenses/Apache-2.0">Apache License 2.0</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;Private-PGM is a post-processing method that is used to estimate a high-dimensional data distribution from noisy measurements of its marginals.&lt;/p&gt;</description>
    <description descriptionType="Other">If you use this software, please cite it using these metadata, as well as the associated publication in the Journal of Privacy and Confidentiality.</description>
  </descriptions>
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