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master's thesis
Anonimizacija podataka u postupcima strojnog učenja
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2021. urn:nbn:hr:168:436754
Nadarević, Patricia
University of Zagreb
Faculty of Electrical Engineering and Computing

Institutional repository: FER Repository

Cite this document

Nadarević, P. (2021). Anonimizacija podataka u postupcima strojnog učenja (Master's thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:436754

Nadarević, Patricia. "Anonimizacija podataka u postupcima strojnog učenja." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2021. https://urn.nsk.hr/urn:nbn:hr:168:436754

Nadarević, Patricia. "Anonimizacija podataka u postupcima strojnog učenja." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2021. https://urn.nsk.hr/urn:nbn:hr:168:436754

Nadarević, P. (2021). 'Anonimizacija podataka u postupcima strojnog učenja', Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 28 December 2024, https://urn.nsk.hr/urn:nbn:hr:168:436754

Nadarević P. Anonimizacija podataka u postupcima strojnog učenja [Master's thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2021 [cited 2024 December 28] Available at: https://urn.nsk.hr/urn:nbn:hr:168:436754

P. Nadarević, "Anonimizacija podataka u postupcima strojnog učenja", Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2021. Available at: https://urn.nsk.hr/urn:nbn:hr:168:436754

Metadata
TitleAnonimizacija podataka u postupcima strojnog učenja
Title (english)Data anonymization in machine learning methods
AuthorPatricia Nadarević
MentorBoris Vrdoljak (mentor)
Committee memberBoris Vrdoljak (predsjednik povjerenstva)
Committee memberDalibor Krleža (član povjerenstva)
Committee memberMirta Baranović (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2021-07-06, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract
S povećanjem količine dostupnih podataka i napredovanjem računala, primjena strojnog učenja doživjela je nevjerojatan rast u akademskim istraživanjima i u stvarnom životu, a performanse modela strojnog učenja uvelike ovise upravo o podacima nad kojima model uči. Glavni problem anonimizacije u kontekstu strojnog učenja je pronaći balans između korisnosti i sigurnosti – kako zaštititi podatke, a da se istovremeno sačuva dovoljna količina korisnih informacija na temelju kojih model može učiti. U sklopu ovog diplomskog rada provedeni su postupci analize i obrade nad dva skupa podataka – Melbourne Housing Market i Adult Income, koristeći Python programski jezik, nakon čega su izgrađeni modeli linearne regresije za određivanje cijene nekretnine, klasifikacijski model za određivanje godišnjih primanja te model LSTM mreže. Također su istraženi različiti pristupi anonimizacije podataka koji su potom i primijenjeni nad korištenim skupovima podataka. Primijenjene su Python biblioteke za k-anonimizaciju, kojima su podaci anonimizirani metodama generalizacije, maskiranja i supresije. Primijenjena je i metoda generiranja sintetičkih podataka. Naposljetku, nad anonimiziranim podacima ponovno su izgrađeni modeli strojnog i dubokog učenja, kako bi se vidio utjecaj anonimizacije na točnost modela.
Abstract (english)
With the increase in the amount of data available and the advancement of computers, the application of machine learning has experienced incredible growth in academic research and in real life, and the performance of the models largely depend on the training data. The main problem of anonymization in the context of machine learning is finding a balance between utility and security - how to protect data while preserving a sufficient amount of useful information from which the model can learn. As part of this thesis data analysis and processing procedures were performed on two datasets – Melbourne Housing Market and Adult Income, using Python programming language, after which models of linear regression for determining the price of real estate, classification model for determining annual income and LSTM network model were built. Different approaches to data anonymization were investigated, which were then applied to the datasets used. Python libraries used for k-anonymization, in which data was anonymized using methods of generalization, masking and suppression. The method of generating synthetic data was also applied. Finally, machine and deep learning models were rebuilt over anonymized data, to see the impact of anonymization on model accuracy.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:168:436754
Study programmeTitle: Computing
Study programme type: university
Study level: graduate
Academic / professional title: magistar/magistra inženjer/inženjerka računarstva (magistar/magistra inženjer/inženjerka računarstva)
Type of resourceText
File originBorn digital
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Created on2023-01-13 13:18:29