Differentially private nearest neighbor classification

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dc.contributor.authorGursoy, Mehmet Emre
dc.contributor.authorInan, Ali
dc.contributor.authorNergiz, Mehmet Ercan
dc.contributor.authorSaygin, Yucel
dc.date.accessioned2025-01-06T17:37:22Z
dc.date.available2025-01-06T17:37:22Z
dc.date.issued2017
dc.descriptionECML PKDD Conference -- SEP 18-22, 2017 -- Skopje, MACEDONIA
dc.description.abstractInstance-based learning, and the k-nearest neighbors algorithm (k-NN) in particular, provide simple yet effective classification algorithms for data mining. Classifiers are often executed on sensitive information such as medical or personal data. Differential privacy has recently emerged as the accepted standard for privacy protection in sensitive data. However, straightforward applications of differential privacy to k-NN classification yield rather inaccurate results. Motivated by this, we develop algorithms to increase the accuracy of private instance-based classification. We first describe the radius neighbors classifier (r-N) and show that its accuracy under differential privacy can be greatly improved by a non-trivial sensitivity analysis. Then, for k-NN classification, we build algorithms that convert k-NN classifiers to r-N classifiers. We experimentally evaluate the accuracy of both classifiers using various datasets. Experiments show that our proposed classifiers significantly outperform baseline private classifiers (i.e., straightforward applications of differential privacy) and executing the classifiers on a dataset published using differential privacy. In addition, the accuracy of our proposed k-NN classifiers are at least comparable to, and in many cases better than, the other differentially private machine learning techniques.
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.doi10.1007/s10618-017-0532-z
dc.identifier.endpage1575
dc.identifier.issn1384-5810
dc.identifier.issn1573-756X
dc.identifier.issue5
dc.identifier.scopus2-s2.0-85025447482
dc.identifier.scopusqualityQ1
dc.identifier.startpage1544
dc.identifier.urihttps://doi.org/10.1007/s10618-017-0532-z
dc.identifier.urihttps://hdl.handle.net/20.500.14669/2208
dc.identifier.volume31
dc.identifier.wosWOS:000408621500015
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherSpringer
dc.relation.ispartofData Mining and Knowledge Discovery
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241211
dc.subjectData mining
dc.subjectDifferential privacy
dc.subjectk-Nearest neighbors
dc.titleDifferentially private nearest neighbor classification
dc.typeConference Object

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