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Code and data for INSPECTRE: Privately Estimating the Unseen

Acharya, Jayadev; Gautam Kamath; Sun, Ziteng; Zhang, Huanyu


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  <identifier identifierType="DOI">10.5281/zenodo.3743892</identifier>
  <creators>
    <creator>
      <creatorName>Acharya, Jayadev</creatorName>
      <givenName>Jayadev</givenName>
      <familyName>Acharya</familyName>
      <affiliation>Cornell University</affiliation>
    </creator>
    <creator>
      <creatorName>Gautam Kamath</creatorName>
      <affiliation>University of Waterloo</affiliation>
    </creator>
    <creator>
      <creatorName>Sun, Ziteng</creatorName>
      <givenName>Ziteng</givenName>
      <familyName>Sun</familyName>
      <affiliation>Cornell University</affiliation>
    </creator>
    <creator>
      <creatorName>Zhang, Huanyu</creatorName>
      <givenName>Huanyu</givenName>
      <familyName>Zhang</familyName>
      <affiliation>Cornell University</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Code and data for INSPECTRE: Privately Estimating the Unseen</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2020</publicationYear>
  <dates>
    <date dateType="Issued">2020-04-08</date>
  </dates>
  <resourceType resourceTypeGeneral="Software"/>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/3743892</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsSupplementTo">https://github.com/journalprivacyconfidentiality/INSPECTRE/tree/v202004-jpc</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsSupplementTo" resourceTypeGeneral="Text">10.29012/jpc.724</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.5281/zenodo.3743891</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://zenodo.org/communities/jpc</relatedIdentifier>
  </relatedIdentifiers>
  <version>v202004-jpc</version>
  <rightsList>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;Code and data for the published article.&lt;/p&gt;

&lt;p&gt;We develop differentially private methods for estimating various distributional properties. Given a sample from a discrete distribution p, some functional f, and accuracy and privacy parameters alpha and epsilon, the goal is to estimate f(p) up to accuracy alpha, while maintaining epsilon-differential privacy of the sample. We prove almost-tight bounds on the sample size required for this problem for several functionals of interest, including support size, support coverage, and entropy. We show that the cost of privacy is negligible in a variety of settings, both theoretically and experimentally. Our methods are based on a sensitivity analysis of several state-of-the-art methods for estimating these properties with sublinear sample complexities&lt;/p&gt;</description>
    <description descriptionType="Other">Funding:  
Office of Naval Research N00014-12-1-0999
National Science Foundation 	CCF-1657471;CCF-1617730;CCF-1650733;CCF-1741137</description>
  </descriptions>
</resource>
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