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Cluster Computing
Erschienen in: Cluster Computing 1/2019

08.03.2018

Lossless and robust privacy preservation of association rules in data sanitization

verfasst von: Geeta S. Navale, Suresh N. Mali

Erschienen in: Cluster Computing | Sonderheft 1/2019

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Abstract

Data sanitization is a novel research area that conceals the sensitive rules given by the experts present in the original database with the appropriate modifications and then emancipates the modified database so that unauthorized persons cannot discover the sensitive rules and so the confidentiality of data is conserved against data mining methods. This paper primarily focuses on building an effective sanitizing algorithm for hiding the sensitive rules given by the experts/users. In order to minimize the four sanitization research challenges such as hiding failure, information loss, false rule generation and modification degree, the proposed method uses Firefly optimization algorithm. The proposed sanitization method has been compared and examined with other existing sanitizing algorithms depicting considerable improvement in terms of four research challenges that in turn can secure the selected database.
Vorheriger Artikel FDLA: Fractional Dragonfly based Load balancing Algorithm in cluster cloud model
Nächster Artikel Clustered entropy computing: a holoentropy based encoding scheme for high efficient computing systems

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Metadaten
Titel
Lossless and robust privacy preservation of association rules in data sanitization
verfasst von
Geeta S. Navale
Suresh N. Mali
Publikationsdatum
08.03.2018
Verlag
Springer US
Erschienen in
Cluster Computing / Ausgabe Sonderheft 1/2019
Print ISSN: 1386-7857
Elektronische ISSN: 1573-7543
DOI
https://doi.org/10.1007/s10586-018-2176-1

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