1 Ocak 2017 Dergi makalesi Açık Erişim

Gursoy, Mehmet Emre; Inan, Ali; Nergiz, Mehmet Ercan; Saygin, Yucel

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  <identifier identifierType="URL">https://aperta.ulakbim.gov.tr/record/47823</identifier>
  <creators>
    <creator>
      <creatorName>Gursoy, Mehmet Emre</creatorName>
      <givenName>Mehmet Emre</givenName>
      <familyName>Gursoy</familyName>
      <affiliation>Georgia Inst Technol, Coll Comp, Atlanta, GA 30332 USA</affiliation>
    </creator>
    <creator>
      <creatorName>Inan, Ali</creatorName>
      <givenName>Ali</givenName>
      <familyName>Inan</familyName>
      <affiliation>Adana Sci &amp; Technol Univ, Comp Engn Dept, TR-01180 Adana, Turkey</affiliation>
    </creator>
    <creator>
      <creatorName>Nergiz, Mehmet Ercan</creatorName>
      <givenName>Mehmet Ercan</givenName>
      <familyName>Nergiz</familyName>
      <affiliation>Acadsoft Res, TR-27310 Gaziantep, Turkey</affiliation>
    </creator>
    <creator>
      <creatorName>Saygin, Yucel</creatorName>
      <givenName>Yucel</givenName>
      <familyName>Saygin</familyName>
      <affiliation>Sabanci Univ, Fac Engn &amp; Nat Sci, TR-34956 Istanbul, Turkey</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Privacy-Preserving Learning Analytics: Challenges And Techniques</title>
  </titles>
  <publisher>Aperta</publisher>
  <publicationYear>2017</publicationYear>
  <dates>
    <date dateType="Issued">2017-01-01</date>
  </dates>
  <resourceType resourceTypeGeneral="Text">Journal article</resourceType>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://aperta.ulakbim.gov.tr/record/47823</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsIdenticalTo">10.1109/TLT.2016.2607747</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">Educational data contains valuable information that can be harvested through learning analytics to provide new insights for a better education system. However, sharing or analysis of this data introduce privacy risks for the data subjects, mostly students. Existing work in the learning analytics literature identifies the need for privacy and pose interesting research directions, but fails to apply state of the art privacy protection methods with quantifiable and mathematically rigorous privacy guarantees. This work aims to employ and evaluate such methods on learning analytics by approaching the problem from two perspectives: (1) the data is anonymized and then shared with a learning analytics expert, and (2) the learning analytics expert is given a privacy-preserving interface that governs her access to the data. We develop proof-of-concept implementations of privacy preserving learning analytics tasks using both perspectives and run them on real and synthetic datasets. We also present an experimental study on the trade-off between individuals' privacy and the accuracy of the learning analytics tasks.</description>
  </descriptions>
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