Software Open Access

Private-PGM

McKenna, Ryan; Miklau, Gerome; Sheldon, Daniel


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  <dc:creator>McKenna, Ryan</dc:creator>
  <dc:creator>Miklau, Gerome</dc:creator>
  <dc:creator>Sheldon, Daniel</dc:creator>
  <dc:date>2021-10-04</dc:date>
  <dc:description>Private-PGM is a post-processing method that is used to estimate a high-dimensional data distribution from noisy measurements of its marginals.</dc:description>
  <dc:description>If you use this software, please cite it using these metadata, as well as the associated publication in the Journal of Privacy and Confidentiality.</dc:description>
  <dc:identifier>https://zenodo.org/record/5548533</dc:identifier>
  <dc:identifier>10.5281/zenodo.5548533</dc:identifier>
  <dc:identifier>oai:zenodo.org:5548533</dc:identifier>
  <dc:relation>url:https://github.com/journalprivacyconfidentiality/private-pgm-jpc-778/tree/v2021-10-04-jpc</dc:relation>
  <dc:relation>doi:10.29012/jpc.778</dc:relation>
  <dc:relation>doi:10.5281/zenodo.5548532</dc:relation>
  <dc:relation>url:https://zenodo.org/communities/jpc</dc:relation>
  <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
  <dc:rights>https://opensource.org/licenses/Apache-2.0</dc:rights>
  <dc:source>Journal of Privacy and Confidentiality 11(3)</dc:source>
  <dc:title>Private-PGM</dc:title>
  <dc:type>info:eu-repo/semantics/other</dc:type>
  <dc:type>software</dc:type>
</oai_dc:dc>
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