A Survey: Deriving Private Information from Perturbed Data

dc.authorid0000-0003-2919-6011
dc.contributor.authorOkkalıoğlu, Burcu D.
dc.contributor.authorOkkalıoğlu, Murat
dc.contributor.authorKoc, Mehmet
dc.contributor.authorPolat, Hüseyin
dc.date.accessioned2021-12-21T08:21:28Z
dc.date.available2021-12-21T08:21:28Z
dc.date.issued2015en_US
dc.departmentFakülteler, Mühendislik Fakültesi, Elektrik Elektronik Mühendisliği Bölümü
dc.description.abstractPrivacy-preserving data mining has attracted the attention of a large number of researchers.Many data perturbation methods have been proposed to ensure individual privacy. Such methods seem to be successful in providing privacy and accuracy. On one hand, different methods are utilized to preserve privacy. On the other hand, various data reconstruction approaches have been proposed to derive private information from perturbed data. Thus, many researchers have been conducting various studies about data reconstruction methods and the resilience of data perturbation schemes. In this survey, we focus on data reconstruction methods due to their importance in privacy-preserving data mining. We provide a detailed review of the data reconstruction methods and the data perturbation schemes attacked by different data reconstruction techniques. We merge our review with the evaluation metrics and the data sets used in current attack techniques. Finally, we pose some open questions to provide a better understanding of these approaches and to guide future studyen_US
dc.identifier.citationOkkalioglu, B. D., Okkalioglu, M., Koc, M., & Polat, H. (2015). A survey: deriving private information from perturbed data. Artificial Intelligence Review, 44(4), 547-569.en_US
dc.identifier.doi10.1007/s10462-015-9439-5
dc.identifier.endpage569en_US
dc.identifier.issn0269-2821
dc.identifier.issn1573-7462
dc.identifier.issue4en_US
dc.identifier.scopus2-s2.0-84945475814
dc.identifier.scopusqualityQ1
dc.identifier.startpage547en_US
dc.identifier.urihttps://doi.org/10.1007/s10462-015-9439-5
dc.identifier.urihttps://hdl.handle.net/11552/2268
dc.identifier.volume44en_US
dc.identifier.wosWOS:000363953700005
dc.identifier.wosqualityQ2
dc.indekslendigikaynakScopus
dc.indekslendigikaynakWoS
dc.indekslendigikaynakWoS - Science Citation Index Expanded
dc.institutionauthorKoç, Mehmet
dc.language.isoen
dc.publisherSpringeren_US
dc.relation.ispartofArtificial Intelligence Review
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectData Reconstructionen_US
dc.subjectData Perturbationen_US
dc.subjectPrivacyen_US
dc.subjectAttack Resilienceen_US
dc.subjectSpectral Filteringen_US
dc.titleA Survey: Deriving Private Information from Perturbed Data
dc.typeArticle

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