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Title:Zasebnost podatkov v odprtih podatkovnih množicah: tveganja, zaščitne tehnike in analiza primerov : magistrsko delo
Authors:ID Povše, Žan (Author)
ID Nemec Zlatolas, Lili (Mentor) More about this mentor... New window
ID Kompara, Marko (Comentor)
Files:.pdf MAG_Povse_Zan_2025.pdf (2,11 MB)
MD5: B1E0AD9B49B57EB9AECBCFEF63634BEA
 
Language:Slovenian
Work type:Master's thesis/paper
Typology:2.09 - Master's Thesis
Organization:FERI - Faculty of Electrical Engineering and Computer Science
Abstract:Magistrsko delo z naslovom Zasebnost podatkov v odprtih podatkovnih množicah: tveganja, anonimizacijske tehnike in analiza primerov obravnava problematiko varovanja zasebnosti v kontekstu odprtih podatkov. V teoretičnem delu so predstavljena glavna tveganja, povezana z objavo podatkov ter različne tehnike za zaščito zasebnosti, kot so: anonimizacija, psevdonimizacija in druge sodobne metode. Poseben poudarek je namenjen oceni učinkovitosti teh tehnik ter morebitnim tveganjem, ki kljub uporabi zaščitnih ukrepov ostajajo prisotna. V empiričnem delu smo izvedli analizo izbranih odprtih podatkovnih množic in prikazali možnost ponovne identifikacije posameznikov, kar poudarja resnost izzivov na področju varovanja zasebnosti. Prikazali smo postopek pravilne anonimizacije podatkov kot primer dobre prakse. V nalogi je predstavljen pravni okvir za varstvo osebnih podatkov na ravni Evropske unije in Republike Slovenije, Splošne uredbe o varstvu podatkov (GDPR). Cilj naloge je bil osvetliti aktualno stanje, opozoriti na potencialna tveganja ter prikazati konkretne rešitve za odgovorno ravnanje z odprtimi podatki.
Keywords:odprti podatki, zasebnost podatkov, anonimizcija, GDPR, pravni okvir, anonimizacijske tehnike
Place of publishing:Maribor
Place of performance:Maribor
Publisher:[Ž. Povše]
Year of publishing:2025
Number of pages:1 spletni vir (1 datoteka PDF (X, 82 str.))
PID:20.500.12556/DKUM-94804 New window
UDC:004.6(043.2)
COBISS.SI-ID:259780611 New window
Publication date in DKUM:22.10.2025
Views:134
Downloads:26
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:27.08.2025

Secondary language

Language:English
Title:Data privacy in open datasets: risks, protection techniques, and case analysis
Abstract:The master's thesis entitled Data Privacy in Open Datasets: Risks, Protection Techniques and Case Analysis addresses the issue of privacy protection in the context of open data. The theoretical part presents the main risks associated with the publication of data, as well as various privacy protection techniques, such as anonymization, pseudonymization and other modern methods. Special emphasis is also placed on assessing the effectiveness of these techniques and potential risks that remain present despite the use of protective measures. For the practical part of the thesis, we conducted an analysis of selected open data sets and demonstrated the possibility of re-identification of individuals, which emphasizes the seriousness of the challenges in the field of privacy protection. In addition, we also demonstrated the procedure for correct data anonymization as an example of good practice. The thesis also presents the legal framework for the protection of personal data at the level of the European Union and the Republic of Slovenia, especially in light of the General Data Protection Regulation (GDPR). The aim of the thesis was to shed light on the current situation, draw attention to potential risks and present concrete solutions for the responsible handling of open data.
Keywords:open data, data privacy, anonymization, GDPR, legal framework, protection techniques


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