Explode: An Extensible Platform for Differentially Private Data Analysis

dc.contributor.authorEsmerdag, Emir
dc.contributor.authorGursoy, Mehmet Emre
dc.contributor.authorInan, Ali
dc.contributor.authorSaygin, Yucel
dc.date.accessioned2025-01-06T17:37:29Z
dc.date.available2025-01-06T17:37:29Z
dc.date.issued2016
dc.description16th IEEE International Conference on Data Mining (ICDM) -- DEC 12-15, 2016 -- Barcelona, SPAIN
dc.description.abstractDifferential privacy (DP) has emerged as a popular standard for privacy protection and received great attention from the research community. However, practitioners often find DP cumbersome to implement, since it requires additional protocols (e.g., for randomized response, noise addition) and changes to existing database systems. To avoid these issues we introduce Explode, a platform for differentially private data analysis. The power of Explode comes from its ease of deployment and use: The data owner can install Explode on top of an SQL server, without modifying any existing components. Explode then hosts a web application that allows users to conveniently perform many popular data analysis tasks through a graphical user interface, e.g., issuing statistical queries, classification, correlation analysis. Explode automatically converts these tasks to collections of SQL queries, and uses the techniques in [3] to determine the right amount of noise that should be added to satisfy DP while producing high utility outputs. This paper describes the current implementation of Explode, together with potential improvements and extensions.
dc.description.sponsorshipIEEE,IEEE Comp Soc,Natl Sci Fdn,Pinnacle Lab
dc.description.sponsorshipScientific and Technological Research Council of Turkey (TUBITAK) [114E261]
dc.description.sponsorshipThis research was funded by The Scientific and Technological Research Council of Turkey (TUBITAK) under grant number 114E261.
dc.identifier.doi10.1109/ICDMW.2016.0189
dc.identifier.endpage1303
dc.identifier.isbn978-1-5090-5910-2
dc.identifier.issn2375-9232
dc.identifier.scopus2-s2.0-85015211312
dc.identifier.scopusqualityN/A
dc.identifier.startpage1300
dc.identifier.urihttps://doi.org/10.1109/ICDMW.2016.0189
dc.identifier.urihttps://hdl.handle.net/20.500.14669/2236
dc.identifier.wosWOS:000401906900181
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherIEEE
dc.relation.ispartof2016 Ieee 16th International Conference on Data Mining Workshops (Icdmw)
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20241211
dc.subjectDifferential privacy
dc.subjectprivacy protection
dc.subjectdata mining
dc.subjectrelational databases
dc.titleExplode: An Extensible Platform for Differentially Private Data Analysis
dc.typeConference Object

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