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Journals / İstanbul Ticaret Üniversitesi Fen Bilimleri Dergisi / 2025 / Cilt: 24 Sayı: 48

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

İŞLETMELER ARASI HETEROJEN VERİLERİN GİZLİLİĞİNİ KORUMAYA YÖNELİK ÖĞRENME MODEL ÇERÇEVESİ

Arafat Salih Aydıner(İSTANBUL MEDENİYET ÜNİVERSİTESİ, SİYASAL BİLGİLER FAKÜLTESİ, İŞLETME BÖLÜMÜ, YÖNETİM BİLİŞİM SİSTEMLERİ ANABİLİM DALI)0000-0002-1133-5995
Pages
725–743
Publication Date
2025-12-18
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

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.

📌 3 Key Takeaways

🎯
Objective & Hypothesis: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.
📊
Key Findings & Data: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.
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Conclusion & Impact: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.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Veri gizliliği · Gizlilik Koruma Teknikleri · Federe Öğrenme

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.

Özet

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.

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Cite This Article

Aydıner, A. S. (2025). A LEARNING MODEL FRAMEWORK FOR PRIVACY PRESERVATION OF HETEROGENEOUS DATA BETWEEN BUSINESSES. İstanbul Ticaret Üniversitesi Fen Bilimleri Dergisi, Cilt: 24 Sayı: 48, 725–743. https://doi.org/10.55071/ticaretfbd.1762972

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References (41)

  1. Acar, A., Aksu, H., Uluagac, A. S., & Conti, M. (2018). A survey on homomorphic encryption schemes: Theory and implementation. ACM Computing Surveys, 51(4). https://doi.org/10.1145/3214303
  2. Acquisti, A., Brandimarte, L., & Loewenstein, G. (2015). Age of information. Science, 347(6221), 509–514. https://doi.org/10.1017/9781108943321
  3. Al-Rubaie, M., & Chang, J. M. (2019). Privacy-Preserving Machine Learning: Threats and Solutions. IEEE Security and Privacy, 17(2), 49–58. https://doi.org/10.1109/MSEC.2018.2888775
  4. Barker, K., Askari, M., Banerjee, M., Ghazinour, K., MacKas, B., Majedi, M., Pun, S., & Williams, A. (2009). A data privacy taxonomy. Lecture Notes in Computer Science (subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 5588 LNCS, 42–54. https://doi.org/10.1007/978-3-642-02843-4_7
  5. Baumeister, R. F., & Leary, M. R. (1997). Writing narrative literature reviews. Review of General Psychology, 1(3), 311–320. https://doi.org/10.1037/1089-2680.1.3.311
  6. Ben-Sasson, E., Chiesa, A., Garman, C., Green, M., Miers, I., Tromer, E., & Virza, M. (2014). Zerocash: Decentralized anonymous payments from bitcoin. Proceedings - IEEE Symposium on Security and Privacy, 459–474. https://doi.org/10.1109/SP.2014.36
  7. Briggs, C., Fan, Z., & Andras, P. (2020). Federated learning with hierarchical clustering of local updates to improve training on non-IID data. Proceedings of the International Joint Conference on Neural Networks. https://doi.org/10.1109/IJCNN48605.2020.9207469
  8. Bunz, B., Bootle, J., Boneh, D., Poelstra, A., Wuille, P., & Maxwell, G. (2018). Bulletproofs: Short Proofs for Confidential Transactions and More. Proceedings - IEEE Symposium on Security and Privacy, 2018-May, 315–334. https://doi.org/10.1109/SP.2018.00020
  9. Chamikara, M. A. P., Bertok, P., Khalil, I., Liu, D., & Camtepe, S. (2021a). PPaaS: Privacy Preservation as a Service. Computer Communications, 173(December 2020), 192–205. https://doi.org/10.1016/j.comcom.2021.04.006
  10. Chamikara, M. A. P., Bertok, P., Khalil, I., Liu, D., & Camtepe, S. (2021b). Privacy preserving distributed machine learning with federated learning. Computer Communications, 171(April 2020), 112–125. https://doi.org/10.1016/j.comcom.2021.02.014
  11. Chen, Y., Qin, X., Wang, J., Yu, C., & Gao, W. (2020). FedHealth: A Federated Transfer Learning Framework for Wearable Healthcare. IEEE Intelligent Systems, 35(4), 83–93. https://doi.org/10.1109/MIS.2020.2988604
  12. Dülger, M. V. (2021). Sağlık Hukukunda Kişisel Verilerin Korunması ve Hasta Mahremiyeti (Sağlık Hukukunda Kişisel Verilerin Korunması ve Hasta Mahremiyeti. Ssrn, 1(2), 43–80. https://ssrn.com/abstract=3792208%0Ahttp://dx.doi.org/10.2139/ssrn.3792208
  13. Dwork, C., & Roth, A. (2014). The algorithmic foundations of differential privacy. Foundations and Trends in Theoretical Computer Science, 9(3–4), 211–487. https://doi.org/10.1561/0400000042
  14. European Parliament & Council of the European Union. (2016). General Data Protection Regulation. Official Journal of the European Union, 2014(March 2014).
  15. Fano, R. M. (1968). The balance of knowledge and the balance of power. Scientific American, 218(5), 149–152.
  16. Giancaspro, M. (2017). Is a ‘smart contract’ really a smart idea? Insights from a legal perspective. Computer Law and Security Review, 33(6), 825–835. https://doi.org/10.1016/j.clsr.2017.05.007
  17. Guo, W., Wang, Y., Chen, X., & Jiang, P. (2024). Federated transfer learning for auxiliary classifier generative adversarial networks: framework and industrial application. Journal of Intelligent Manufacturing, 35(4), 1439–1454. https://doi.org/10.1007/s10845-023-02126-z
  18. Guo, W., Zhuang, F., Zhang, X., Tong, Y., & Dong, J. (2024). A comprehensive survey of federated transfer learning: challenges, methods and applications. Frontiers of Computer Science, 18(6). https://doi.org/10.1007/s11704-024-40065-x
  19. Hassan, M. U., Rehmani, M. H., & Chen, J. (2020). Differential Privacy Techniques for Cyber Physical Systems: A Survey. IEEE Communications Surveys and Tutorials, 22(1), 746–789. https://doi.org/10.1109/COMST.2019.2944748
  20. Kardaş, S., & Kiraz, M. (2018). Bitcoin’DMahremi̇yeti̇ SağlamaYöntemleri̇. Uluslararası Bilgi Güvenliği Mühendisliği Dergisi, 4(1), 1–9. https://doi.org/10.18640/ubgmd.429461
  21. Keshk, M., Turnbull, B., Moustafa, N., Vatsalan, D., & Choo, K. K. R. (2020). A Privacy-Preserving-Framework-Based Blockchain and Deep Learning for Protecting Smart Power Networks. IEEE Transactions on Industrial Informatics, 16(8), 5110–5118. https://doi.org/10.1109/TII.2019.2957140
  22. Khalid, N., Qayyum, A., Bilal, M., Al-Fuqaha, A., & Qadir, J. (2023). Privacy-preserving artificial intelligence in healthcare: Techniques and applications. Computers in Biology and Medicine, 158(April), 106848. https://doi.org/10.1016/j.compbiomed.2023.106848
  23. Li, T., Sahu, A. K., Talwalkar, A., & Smith, V. (2020). Federated Learning: Challenges, Methods, and Future Directions. IEEE Signal Processing Magazine, 37(3), 50–60. https://doi.org/10.1109/MSP.2020.2975749
  24. Li, X., Chi, H. lin, Lu, W., Xue, F., Zeng, J., & Li, C. Z. (2021). Federated transfer learning enabled smart work packaging for preserving personal image information of construction worker. Automation in Construction, 128(January), 103738. https://doi.org/10.1016/j.autcon.2021.103738
  25. Liu, L., Zhang, J., Song, S. H., & Letaief, K. B. (2020). Client-Edge-Cloud Hierarchical Federated Learning. IEEE International Conference on Communications, 2020-June. https://doi.org/10.1109/ICC40277.2020.9148862
  26. Marijan, D., & Lal, C. (2022). Blockchain verification and validation: Techniques, challenges, and research directions. Computer Science Review, 45, 100492. https://doi.org/10.1016/j.cosrev.2022.100492
  27. McMahan, H. B., Moore, E., Ramage, D., Hampson, S., & Arcas, B. A. y. (2017). Communication-Efficient Learning of Deep Networks from Decentralized Data. Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, 54, 10.
  28. Metin, B., Erkan, S., Atasu, İ., & Yılmaz, E. (2019). Privacy Impact Assessment as a Tool for GDPR Compliance Preparation. Kişisel Verileri Koruma Dergisi, 1(2), 75–86.
  29. Mulligan, D. K., Koopman, C., & Doty, N. (2016). Privacy is an essentially contested concept: A multi-dimensional analytic for mapping privacy. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 374(2083). https://doi.org/10.1098/rsta.2016.0118
  30. Nissim, K., & Wood, A. (2018). Is privacy privacy? Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 376(2128).
  31. Oktay, E. (2022). Genomik Çalişmalarda Kullanilan Makine Öğrenmesi Modellerinin Blokzincir Temelli Bir Eko Sistemde Federe Öğrenme Yöntemiyle Geliştirilmesi İçin Kavramsal Bir Çalişma. Istanbul Medeniyet Üniversitesi.
  32. Özcandan, N., Kalkar, Ö., Bingöl, M. A., Sertaya, İ., Dursun, T., Yıldız, O., & Türk, E. (2020). Blokzincirlerde Güvenlik ve Mahremiyet.
  33. Pouriyeh, S., Shahid, O., Parizi, R. M., Sheng, Q. Z., Srivastava, G., Zhao, L., & Nasajpour, M. (2022). Secure Smart Communication Efficiency in Federated Learning: Achievements and Challenges. Applied Sciences (Switzerland), 12(18). https://doi.org/10.3390/app12188980
  34. Putra, M. A. P., Rachmawati, S. M., Abisado, M., & Sampedro, G. A. (2023). HFTL: Hierarchical Federated Transfer Learning for Secure and Efficient Fault Classification in Additive Manufacturing. IEEE Access, 11, 54795–54807. https://doi.org/10.1109/ACCESS.2023.3280471
  35. Ram Mohan Rao, P., Murali Krishna, S., & Siva Kumar, A. P. (2018). Privacy preservation techniques in big data analytics: a survey. Journal of Big Data, 5(1). https://doi.org/10.1186/s40537-018-0141-8
  36. Shin, D. D. H. (2019). Blockchain: The emerging technology of digital trust. Telematics and Informatics, 45(August). https://doi.org/10.1016/j.tele.2019.101278
  37. Solove, D. J. (2008). Understanding Privacy. Massachusetts: Harvard University Press.
  38. Türkiye Büyük Millet Meclisi (T.B.M.M). (2016). Kişisel verilerin korunmasi kanunu. Resmi Gazete, 12301–12317.
  39. Zhang, Z., Ming, Y., & Wang, Y. (2024). A federated transfer learning approach for surface electromyographic hand gesture recognition with emphasis on privacy preservation. Engineering Applications of Artificial Intelligence, 136(PA), 108952. https://doi.org/10.1016/j.engappai.2024.108952
  40. Zheng, G., Kong, L., & Brintrup, A. (2023). Federated machine learning for privacy preserving, collective supply chain risk prediction. International Journal of Production Research, 61(23), 8115–8132. https://doi.org/10.1080/00207543.2022.2164628
  41. Zhou, X., Ye, X., Wang, K. I. K., Liang, W., Nair, N. K. C., Shimizu, S., Yan, Z., & Jin, Q. (2023). Hierarchical Federated Learning With Social Context Clustering-Based Participant Selection for Internet of Medical Things Applications. IEEE Transactions on Computational Social Systems, 10(4), 1742–1751. https://doi.org/10.1109/TCSS.2023.3259431