İŞLETMELER ARASI HETEROJEN VERİLERİN GİZLİLİĞİNİ KORUMAYA YÖNELİK ÖĞRENME MODEL ÇERÇEVESİ

Year 2025, Volume: 24 Issue: 48 , 725 - 743 , 18.12.2025

Arafat Salih Aydıner

https://doi.org/10.55071/ticaretfbd.1762972

https://izlik.org/JA68EA28SY

Abstract

Yapay zekâ uygulamalarının hızla yaygınlaştığı bir çağda, veri kullanımının yaygınlaşması ve kişisel hakların korunmasının giderek zorlaşması, bu alanın doğası gereği dijital kişisel veri koruma tekniklerinin geliştirilmesinin önünü açmıştır. Bu çalışmada, kavramsal analiz kullanılarak literatürden kişisel verilerin korunmasına yönelik gizlilik koruma teknikleri çıkarılmıştır. Literatür analizinden 30 farklı makaleye dayanarak, aynı amaç için farklı verilerin eğitilmesine olanak tanıyan Federe Transfer Öğrenme yöntemini tanımlayan bir model önerisi geliştirilmiştir. Böylece çalışma, yerel verileri paylaşmadan çeşitli verilerin kullanılmasını sağlayarak ve ortak sorunların çözümü için gizlilik koruması ile karar alma desteği sağlayarak sahadaki pratik veri kullanım zorluklarını ele alan teorik ve pratik katkılar sağlayacaktır.

Keywords

Veri gizliliği , Gizlilik Koruma Teknikleri , Federe Öğrenme , Federe Transfer Öğrenme , Heterojen Veriler

A LEARNING MODEL FRAMEWORK FOR PRIVACY PRESERVATION OF HETEROGENEOUS DATA BETWEEN BUSINESSES

https://izlik.org/JA68EA28SY

Abstract

In the era of rapid artificial intelligence implementations, the proliferation of data use and the increasing difficulty of protecting personal rights, coupled with its inherent nature, have paved the way for the development of digital personal data protection techniques. In this study, privacy preservation techniques for protecting personal data were extracted from the literature using conceptual analysis. A model proposal was developed based on 30 different articles from the literature analysis, identifying the Federated Transfer Learning method, which enables training different data for the same purpose. Thus, the study will provide theoretical and practical contributions that address practical data usage challenges in the field by enabling the use of diverse data without sharing local data and providing privacy preservation with decision-making support for solving common problems.

Keywords

Data Privacy , Privacy Preservation Techniques , Federated Learning , Federated Transfer Learning , Heterogeneous Data

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