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Comparative study of k-anonymity algorithms for privacy preserving datamining

Abd. Elrahman, Osman Abbas Elsheikh Idris (2013) Comparative study of k-anonymity algorithms for privacy preserving datamining. Masters thesis, Universiti Teknologi Malaysia, Faculty of Computing.

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Abstract

Nowadays, privacy issue becomes one of the main concerns of persons among their raw data. this happens at a time, when more and more historically public information is also electronically available. when these data are linked together, they provide an electronic shadow of a person or organization that is as identifying and personal as a fingerprint even when the information contains no explicit identifiers, such as name and phone number. other distinctive data, such as birth date and zip code, often combine uniquely and can be linked to publicly available information to re-identify individuals. however, there are several kanonymity algorithms available in the literature to solve that problem such as datafly and incognito. nevertheless, their study of performances in terms of efficiency and accuracy is lacking. in this study, we compare these two k-anonymity algorithms. so that users can select which algorithm is more suitable for their data mining. the finding shows that datafly gives higher overall efficiency. comparing with incognito which gives high accuracy. consistent good performance of incognito in kanonymity has made a promising k-anonymity techniques to be used in the privacy preserving technique.

Item Type:Thesis (Masters)
Additional Information:Thesis (Sarjana Sains Komputer (Keselamatan Maklumat)) - Universiti Teknologi Malaysia, 2013; Supervisor : Assoc. Prof. Dr. Subariah Ibrahim
Uncontrolled Keywords:data mining, database management
Subjects:Unspecified
Divisions:Computing
ID Code:37908
Deposited By: Siti Zulfah Mohd Basiran
Deposited On:30 Apr 2014 07:50
Last Modified:11 Jul 2017 00:34

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