1 Ocak 2022 Dergi makalesi Açık Erişim

Kuru, Nurdan; Birbil, S. Ilker; Gurbuzbalaban, Mert; Yildirim, Sinan

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  <identifier identifierType="URL">https://aperta.ulakbim.gov.tr/record/255605</identifier>
  <creators>
    <creator>
      <creatorName>Kuru, Nurdan</creatorName>
      <givenName>Nurdan</givenName>
      <familyName>Kuru</familyName>
      <affiliation>Sabanci Univ, Fac Engn &amp; Nat Sci, TR-34956 Istanbul, Turkey</affiliation>
    </creator>
    <creator>
      <creatorName>Birbil, S. Ilker</creatorName>
      <givenName>S. Ilker</givenName>
      <familyName>Birbil</familyName>
      <affiliation>Univ Amsterdam, Amsterdam Business Sch, NL-1018 TV Amsterdam, Netherlands</affiliation>
    </creator>
    <creator>
      <creatorName>Gurbuzbalaban, Mert</creatorName>
      <givenName>Mert</givenName>
      <familyName>Gurbuzbalaban</familyName>
      <affiliation>Rutgers State Univ, Dept Management Sci &amp; Informat Syst, Piscataway, NJ 08854 USA</affiliation>
    </creator>
    <creator>
      <creatorName>Yildirim, Sinan</creatorName>
      <givenName>Sinan</givenName>
      <familyName>Yildirim</familyName>
      <affiliation>Sabanci Univ, Fac Engn &amp; Nat Sci, TR-34956 Istanbul, Turkey</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Differentially Private Accelerated Optimization Algorithms</title>
  </titles>
  <publisher>Aperta</publisher>
  <publicationYear>2022</publicationYear>
  <dates>
    <date dateType="Issued">2022-01-01</date>
  </dates>
  <resourceType resourceTypeGeneral="Text">Journal article</resourceType>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://aperta.ulakbim.gov.tr/record/255605</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsIdenticalTo">10.1137/20M1355847</relatedIdentifier>
  </relatedIdentifiers>
  <rightsList>
    <rights rightsURI="http://www.opendefinition.org/licenses/cc-by">Creative Commons Attribution</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">We present two classes of differentially private optimization algorithms derived from the well-known accelerated first-order methods. The first algorithm is inspired by Polyak's heavy ball method and employs a smoothing approach to decrease the accumulated noise on the gradient steps required for differential privacy. The second class of algorithms are based on Nesterov's accelerated gradient method and its recent multistage variant. We propose a noise dividing mechanism for the iterations of Nesterov's method in order to improve the error behavior of the algorithm. The convergence rate analyses are provided for both the heavy ball and the Nesterov's accelerated gradient method with the help of the dynamical system analysis techniques. Finally, we conclude with our numerical experiments showing that the presented algorithms have advantages over the well-known differentially private algorithms.</description>
  </descriptions>
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