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dc.contributor.authorAlmohaimeed, Abdulrahman
dc.contributor.authorGampa, Srikanth
dc.date.accessioned2020-04-19T16:15:42Z
dc.date.available2020-04-19T16:15:42Z
dc.date.issued2020-03-05
dc.identifier.citationA. Almohaimeed and S. Gampa, "Applying k-Nearest Neighbors to Increase the Utility of k-Anonymity," 2019 SoutheastCon, Huntsville, AL, USA, 2019, pp. 1-3en_US
dc.identifier.isbn978-172810137-8
dc.identifier.issn0734-7502
dc.identifier.urihttps://doi.org/10.1109/SoutheastCon42311.2019.9020525
dc.identifier.urihttps://soar.wichita.edu/handle/10057/17399
dc.descriptionClick on the DOI link to access the article (may not be free).en_US
dc.description.abstractNowadays, there are many organizations publishing and sharing their databases with other parties for different purposes, such as to conduct statistical surveys, business investigations, or health studies. However, this shared information is mostly public, and adversaries can use it to reveal and expose real identities; therefore, it is important for database owners to preserve the privacy of the individual's data. Previous researches in data anonymity have provided different privacy-preserving methods for protecting published data. However, the utility of the anonymized databases remains an important challenge and requires further studies. In this paper, we proposed a new way to increase the utility of the anonymized databases. We integrated kNN, a classification method, with k-anonymity to measure the similarities among multiple records. Finally, we show an example of how kNN can significantly maximizing the utility of the released databases while preserving data privacy.en_US
dc.language.isoen_USen_US
dc.publisherIEEEen_US
dc.relation.ispartofseriesIEEE SoutheastCon;2019
dc.subjectAnonymityen_US
dc.subjectk-Anonymityen_US
dc.subjectk-Nearest Neighborsen_US
dc.subjectPrivacyen_US
dc.subjectPublished dataen_US
dc.titleApplying k-nearest neighbors to increase the utility of k-anonymityen_US
dc.typeConference paperen_US
dc.rights.holder© 2019 IEEEen_US


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