Software Open Access

Private-PGM

McKenna, Ryan; Miklau, Gerome; Sheldon, Daniel


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{
  "description": "<p>Private-PGM is a post-processing method that is used to estimate a high-dimensional data distribution from noisy measurements of its marginals.</p>", 
  "license": "https://opensource.org/licenses/Apache-2.0", 
  "creator": [
    {
      "affiliation": "College of Information and Computer Sciences, The University of Massachusets, Amherst, MA", 
      "@type": "Person", 
      "name": "McKenna, Ryan"
    }, 
    {
      "affiliation": "College of Information and Computer Sciences, The University of Massachusets, Amherst, MA", 
      "@type": "Person", 
      "name": "Miklau, Gerome"
    }, 
    {
      "affiliation": "College of Information and Computer Sciences, The University of Massachusets, Amherst, MA", 
      "@type": "Person", 
      "name": "Sheldon, Daniel"
    }
  ], 
  "url": "https://zenodo.org/record/5548533", 
  "codeRepository": "https://github.com/journalprivacyconfidentiality/private-pgm-jpc-778/tree/v2021-10-04-jpc", 
  "datePublished": "2021-10-04", 
  "version": "v2021-10-04-jpc", 
  "@context": "https://schema.org/", 
  "identifier": "https://doi.org/10.5281/zenodo.5548533", 
  "@id": "https://doi.org/10.5281/zenodo.5548533", 
  "@type": "SoftwareSourceCode", 
  "name": "Private-PGM"
}
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