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undergraduate thesis
Zajedničko strojno učenje uz očuvanje privatnosti
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2019. urn:nbn:hr:168:941591
Škudar, Silvije
University of Zagreb
Faculty of Electrical Engineering and Computing

Institutional repository: FER Repository

Cite this document

Škudar, S. (2019). Zajedničko strojno učenje uz očuvanje privatnosti (Undergraduate thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:941591

Škudar, Silvije. "Zajedničko strojno učenje uz očuvanje privatnosti." Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2019. https://urn.nsk.hr/urn:nbn:hr:168:941591

Škudar, Silvije. "Zajedničko strojno učenje uz očuvanje privatnosti." Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2019. https://urn.nsk.hr/urn:nbn:hr:168:941591

Škudar, S. (2019). 'Zajedničko strojno učenje uz očuvanje privatnosti', Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 15 May 2024, https://urn.nsk.hr/urn:nbn:hr:168:941591

Škudar S. Zajedničko strojno učenje uz očuvanje privatnosti [Undergraduate thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2019 [cited 2024 May 15] Available at: https://urn.nsk.hr/urn:nbn:hr:168:941591

S. Škudar, "Zajedničko strojno učenje uz očuvanje privatnosti", Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2019. Available at: https://urn.nsk.hr/urn:nbn:hr:168:941591

Metadata
TitleZajedničko strojno učenje uz očuvanje privatnosti
Title (english)Privacy-preserving collaborative machine learning
AuthorSilvije Škudar
MentorAnte Đerek (mentor)
Committee memberAnte Đerek (predsjednik povjerenstva)
Committee memberMartin Tutek (član povjerenstva)
Committee memberDarijan Marčetić (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2019-07-12, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract
U radu je opisan i implementiran protokol za zajedničko strojno učenje uz očuvanje privatnosti. Prilikom njegovog opisa, definirani su i kriptografski primitivi koji se koriste u njegovoj implementaciji. Takod̄er su se formalno definirala i analizirala njegovih ciljeva i sigurnosnih svojstava u kontekstu pasivnog i aktivnog suparnika. Dalje, pokazuju se neke osnovne tehnike strojnog učenja i njegova funkcionalnost je testirana na konkretnom primjeru logističke regresije. Konačno, opisuju se neki poznati napadi koji mogu narušiti njegovu funkcionalnost ili privatnost korisničkih podataka.
Abstract (english)
This thesis describes a privacy-preserving collaborative machine learning protocol and provides its implementation. Alongside its description, it also defines the cryptographic primitives used in its implementation. Also, it presents a formal definition and analysis of its goals and security properties in the context of passive and active adversaries. Further, common machine learning techniques are shown and the functionality of the protocol is tested on a concrete implementation of logistic regression. Finally, the thesis describes some known attacks that can compromise its functionality or the privacy of users' data.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:168:941591
Study programmeTitle: Computing
Study programme type: university
Study level: undergraduate
Academic / professional title: sveučilišni/a prvostupnik/ prvostupnica (baccalaureus/baccalaurea) inženjer/inženjerka računarstva (sveučilišni/a prvostupnik/ prvostupnica (baccalaureus/baccalaurea) inženjer/inženjerka računarstva)
Type of resourceText
File originBorn digital
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Created on2020-01-07 22:27:05