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Title:Anonimizacija podatkov v podatkovnih bazah : magistrsko delo
Authors:ID Požun, Žiga (Author)
ID Nemec Zlatolas, Lili (Mentor) More about this mentor... New window
Files:.pdf MAG_Pozun_Ziga_2022.pdf (1,91 MB)
MD5: E9E8F9B66B087B2BD3A889BA09346927
 
Language:Slovenian
Work type:Master's thesis/paper
Typology:2.09 - Master's Thesis
Organization:FERI - Faculty of Electrical Engineering and Computer Science
Abstract:V sklopu magistrskega dela smo se spoznali s pojmom anonimizacija, kjer smo se osredotočili na tri modele zasebnosti: k-anonimnost, l-raznolikost in t-oddaljenost. Uporabili smo bazo rakavih bolnikov, pridobljeno s spleta, ki smo jo s pomočjo dveh anonimizacijskih operacij (prikrivanje in posploševanje) pripravili za implementacijo vseh modelov zasebnosti. Znotraj implementacije smo prikazali svoj pristop, delovanje posameznega modela, njegove prednosti, šibkosti in seznam drugih modelov, ki služijo kot nadgradnje predhodnega. Na koncu smo naredili še povzetek, kjer smo poudarili ključne razlike med uporabljenimi modeli.
Keywords:k-anonimnost, l-raznolikost, t-oddaljenost, anonimizacija, anonimnost podatkov.
Place of publishing:Maribor
Place of performance:Maribor
Publisher:[Ž. Požun]
Year of publishing:2022
Number of pages:1 spletni vir (1 datoteka PDF (VII, 67 f.))
PID:20.500.12556/DKUM-83293 New window
UDC:004.652:004.6-028.51(043.2)
COBISS.SI-ID:146429955 New window
Publication date in DKUM:11.11.2022
Views:874
Downloads:109
Metadata:XML DC-XML DC-RDF
Categories:KTFMB - FERI
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Licences

License:CC BY-NC-ND 4.0, Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
Link:http://creativecommons.org/licenses/by-nc-nd/4.0/
Description:The most restrictive Creative Commons license. This only allows people to download and share the work for no commercial gain and for no other purposes.
Licensing start date:14.10.2022

Secondary language

Language:English
Title:Data anonymization in databases
Abstract:As part of our master's thesis, we learned about the concept of anonymization, where we focused on three models of privacy; k-anonymity, l-diversity and t-closeness. We used a database of cancer patients obtained from the Internet, which we prepared with the help of two anonymization operations (suppression and generalization) for the implementation of all privacy models. Within the implementation, we showed our approach, the operation of each model, its strengths, weaknesses and a list of other models that serve as upgrades to the previous one. At the end, we made a summary, where we highlighted the key differences between the used models.
Keywords:k-anonymity, l-diversity, t-closeness, anonymization, data anonymity.


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