İstanbul Ticaret Üniversitesi Fen Bilimleri Dergisi 1305-7820 2587-165X Istanbul Ticaret University 10.55071/ticaretfbd.1762972 Information Modelling, Management and Ontologies Information Systems Development Methodologies and Practice Information Systems Organisation and Management Data and Information Privacy Bilgi Modelleme, Yönetim ve Ontolojiler Bilgi Sistemleri Geliştirme Metodolojileri ve Uygulamaları Bilgi Sistemleri Organizasyonu ve Yönetimi Veri ve Bilgi Gizliliği İŞLETMELER ARASI HETEROJEN VERİLERİN GİZLİLİĞİNİ KORUMAYA YÖNELİK ÖĞRENME MODEL ÇERÇEVESİ A LEARNING MODEL FRAMEWORK FOR PRIVACY PRESERVATION OF HETEROGENEOUS DATA BETWEEN BUSINESSES https://orcid.org/0000-0002-1133-5995 Aydıner Arafat Salih İSTANBUL MEDENİYET ÜNİVERSİTESİ, SİYASAL BİLGİLER FAKÜLTESİ, İŞLETME BÖLÜMÜ, YÖNETİM BİLİŞİM SİSTEMLERİ ANABİLİM DALI 12 18 2025 24 48 725 743 08 11 2025 11 04 2025 Copyright © 2002, İstanbul Commerce University Journal of Science 2002 İstanbul Commerce University Journal of Science

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.

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.

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