Mahremiyet Artırıcı Teknolojiler, Gizlilikten Ödün Vermeden Yapay Zeka Teknolojisinin Gelişimini ve Kullanımını Nasıl Teşvik Edebilir? Türkiye İçin Bir Politika Önerisi

Yıl 2025, Cilt: 7 Sayı: 2, 173 - 188, 29.12.2025

Hüseyin Can Aksoy

Öz

Yapay zekâ teknolojisinin gelişimi, veri işleme becerimizde devrim yaratarak toplumsal refah, bilginin yayılması ve zenginlik yaratılması için derin bir potansiyelin kilidini açmıştır. Ancak bu sistemlerin yüksek doğrulukta çalışabilmesi için büyük hacimli verilere ihtiyaç duyması, kişisel verilerin korunmasına ilişkin hukuki ilkelerle, özellikle veri minimizasyonu ile yapısal bir gerilim yaratmaktadır. Bu gerilim aynı zamanda yapay zekâya duyulan kamu güvenini de zayıflatmaktadır. Mahremiyet artırıcı teknolojiler (MATlar), tasarımdan itibaren mahremiyet yaklaşımının teknik karşılığı olarak, mahremiyetin korunması ile inovasyonun sürdürülmesi arasında pozitif toplamlı çözümler sunmaktadır. Sentetik veriler, homomorfik şifreleme, farklılaştırılmış gizlilik, federe öğrenme ve benzeri teknolojiler, bir yandan mahremiyeti korurken diğer yandan veri temelli analiz yapılmasını mümkün kılmakta ve mahremiyetten ödün verilmeksizin yapay zekâ teknolojisinden azami fayda sağlanmasına imkân tanımaktadır. Bununla birlikte MATların etkili biçimde benimsenebilmesi, hukuki belirlilik, düzenleyici rehberlik ve uygun teşvik mekanizmalarıyla desteklenen bir politika çerçevesinin oluşturulmasını gerektirmektedir.

Anahtar Kelimeler

Mahremiyet artırıcı teknolojiler , PET , Yapay Zeka , Tasarımdan İtibaren Mahremiyet , Mahremiyet

Destekleyen Kurum

TÜBİTAK

HOW CAN PRIVACY-ENHANCING TECHNOLOGIES PROMOTE THE DEVELOPMENT AND USE OF ARTIFICIAL INTELLIGENCE WITHOUT COMPROMISING PRIVACY? A POLICY PROPOSAL FOR TÜRKİYE

Öz

The development of artificial intelligence technology has revolutionized our capacity to process data, unlocking profound potential for social welfare, the dissemination of knowledge, and the creation of wealth. However, the fact that these systems require large volumes of data to operate with high accuracy creates a structural tension with the legal principles governing the protection of personal data—particularly the principle of data minimization. This tension also undermines public trust in artificial intelligence. Privacy-enhancing technologies (PETs), as the technical embodiment of the privacy-by-design approach, offer positive-sum solutions that reconcile the protection of privacy with the continued pursuit of innovation. Technologies such as synthetic data, homomorphic encryption, differential privacy, federated learning, and similar methods make it possible to conduct data-driven analysis while safeguarding privacy, thereby enabling the maximization of the benefits of artificial intelligence without compromising privacy. Nevertheless, the effective adoption of PETs requires the establishment of a policy framework supported by legal certainty, regulatory guidance, and appropriate incentive mechanisms.

Anahtar Kelimeler

Privacy-Enhancing-Technologies , PET , Privacy , Artificial Intelligence , Privacy by design

Kaynakça

• AEPD. (2019). A Guide to Privacy by Design. https://www.aepd.es/guides/guide-to-privacy-by-design.pdf Son erişim tarihi: 4 Haziran 2025.
• AEPD. (2023). Synthetic data and data protection. https://www.aepd.es/en/prensa-y-comunicacion/blog/synthetic-data-and-data-protection Son erişim tarihi: 17 Mart 2025.
• Aksoy, H. C. (2022). Kişisel Verilerin Korunması Yönüyle Algoritmik Karar Verme. Kişisel Verileri Koruma Dergisi, 4(2).
• Bada, M., et al. (2023). Supporting Small and Medium-Sized Enterprises in Using Privacy Enhancing Technologies. A. Moallem (Ed.), HCI for Cybersecurity, Privacy and Trust (ss. 274–289). Cham: Springer. https://doi.org/10.1007/978-3-031-35822-7_19
• Borealis AI. (2020). RESPECT AI: Privacy, by design. https://www.borealisai.com/news/respect-ai-privacy-design/ Son erişim tarihi: 4 Haziran 2025.
• Borking, J., & Raab, C. D. (2001). Laws, PETs and Other Technologies for Privacy Protection. The Journal of Information, Law and Technology, 2001(1).
• Borking, J. (2002). The Status of Privacy Enhancing Technologies – (PET) Online and Offline.
• E. Nardelli, S. Posadziejewski & M. Talamo (Eds.), Certification and Security in E-Services – From E-Government to E-Business. Boston: Springer.
• Cavoukian, A. (2009). Privacy by design: The 7 foundational principles. UC Santa Cruz. https://privacy.ucsc.edu/resources/privacy-by-design---foundational-principles.pdf Son erişim tarihi: 2 Haziran 2025.
• Cavoukian, A., et al. (2010). Privacy by Design: essential for organizational accountability and strong business practices. Identity in the Information Society, 3(2), 405–413.
• Cavoukian, A. (2012). Operationalizing Privacy by Design: A Guide to Implementing Strong Privacy Practices. https://gpsbydesigncentre.com/wp-content/uploads/2021/08/Doc-5-Operationalizing-pbd-guide.pdf Son erişim tarihi: 2 Haziran 2025.
• CIPL. (2025). Privacy-Enhancing and Privacy-Preserving Technologies in AI: Enabling Data Use and Operationalizing Privacy by Design and Default. Mart 2025.
• D’Acquisto, G. (2024). Synthetic data and data protection laws. Journal of Data Protection & Privacy, 6(3), 227–239.
• d’Aliberti, L., et al. (2024). Privacy-Enhancing Technologies for Artificial Intelligence-Enabled Systems. arXiv:2404.03509v1. https://arxiv.org/html/2404.03509v1
• Dulberg, R. (2021). Privacy Enhancing Technologies and why they’re vital for healthcare innovation. Medium. https://medium.com/codex/ai-privacy-and-why-you-should-care-1ef503a789b6 Son erişim tarihi: 4 Haziran 2025.
• European Data Protection Supervisor. (t.y.). Glossary – Data Minimization. https://edps.europa.eu/data-protection/data-protection/glossary/d_en#data_minimization Son erişim tarihi: 5 Haziran 2025.
• European Data Protection Supervisor. (2018). Opinion 5/2018 – Preliminary Opinion on privacy by design. https://edps.europa.eu/sites/edp/files/publication/18-05-31_preliminary_opinion_on_privacy_by_design_en_0.pdf Son erişim tarihi: 5 Haziran 2025. Fragkouli, S. C., et al. (2024). Synthetic data: how could it be used in infectious disease research? Future Microbiology, 19(17), 1439–1444. https://doi.org/10.1080/17460913.2024.2400853
• Google. (2025). Google, konuşma modellerini nasıl iyileştirir? https://support.google.com/assistant/answer/11140942 Son erişim tarihi: 5 Haziran 2025.
• Gustavsson, S. (2020). An Assessment of Privacy by Design as a Stipulation in GDPR (Yayımlanmamış yüksek lisans tezi). Uppsala Universitesi, İsveç.
• Gürses, S., Troncoso, C., & Diaz, C. (2007). Engineering Privacy by Design. Institute IMDEA Software. https://software.imdea.org/~carmela.troncoso/papers/Gurses-CPDP11.pdf Son erişim tarihi: 2 Haziran 2025.
• Hornung, G. (2013). Regulating privacy enhancing technologies: seizing the opportunity of the future European Data Protection Framework.
• Innovation: The European Journal of Social Science Research, 26(1–2), 181–196.
• Hustinx, P. (2010). Privacy by design: delivering the promises. Identity in the Information Society, 3, 253–255.
• Information Commissioner’s Office. (2023). Privacy-enhancing Technologies (PETs).
• https://ico.org.uk/for-organisations/uk-gdpr-guidance-and-resources/data-sharing/privacy-enhancing-technologies/ Son erişim tarihi: 2 Haziran 2025.
• James, S., Harbron, C., Branson, J., & Sundler, M. (2021). Synthetic data use: exploring use cases to optimise data utility. Discover Artificial Intelligence, 1(1). https://doi.org/10.1007/s44163-021-00016-y
• Kindt, E. (2013). Best Practices for Privacy and Data Protection for the Processing of Biometric Data.
• P. Campisi (Ed.), Security and Privacy in Biometrics. London: Springer.
• Kosinski, M. (2025). Why Synthetic Data is the Future of AI: Exploring the Shift from Real-World Datasets. https://www.1950.ai/post/why-synthetic-data-is-the-future-of-ai-exploring-the-shift-from-real-world-datasets Son erişim tarihi: 14 Mart 2025.
• Kurapati, S., & Gilli, L. (2023). Synthetic Data: Convergence between Innovation and GDPR.
• Journal of Open Access to Law, 11, 1–12.
• London Economics. (2010). Study on the economic benefits of privacy-enhancing technologies (PETs). https://londoneconomics.co.uk/wp-content/uploads/2011/09/17-Study-on-the-economic-benefits-of-privacy-enhancing-technologies-PETs.pdf Son erişim tarihi: 2 Haziran 2025.
• Macgillivray, A. (2022). Advancing a Vision for Privacy-Enhancing Technologies. The White House. https://www.whitehouse.gov/ostp/news-updates/2022/06/28/advancing-a-vision-for-privacy-enhancing-technologies/ Son erişim tarihi: 2 Haziran 2025.
• Morales-Trujillo, M. E., et al. (2019). A Systematic Mapping Study of Privacy by Design in Software Engineering. CLEI Electronic Journal, 22(1), 1–29.
• Nampiina, E. (2020). Privacy by Design – A qualitative study to explore privacy by design adaptation in reducing privacy breaches (Yayımlanmamış yüksek lisans tezi).
• Lund University School of Economics and Management, İsveç.
• Obermeyer, H. (2021). Privacy-Enhancing Technologies: Opening Data, Powering AI. BSA TechPost. https://techpost.bsa.org/2021/02/01/privacy-enhancing-technologies-opening-data-powering-ai/ Son erişim tarihi: 2 Haziran 2025.
• OECD. (2023). Emerging Privacy Enhancing Technologies – Current Regulatory and Policy Approaches. OECD Digital Economy Papers, No. 351.
• OECD. (2025). Sharing Trustworthy AI Models with Privacy-Enhancing Technologies.
• OECD Artificial Intelligence Papers, No. 38.
• Oualha, N. (2025). SoK: Privacy-Enhancing Technologies in Artificial Intelligence. arXiv preprint arXiv:2506.14576.
• PDPC (Personal Data Protection Commission – Singapore). (2024). Privacy Enhancing Technology (PET): Proposed Guide on Synthetic Data Generation. https://www.conformally.com/featured_item/privacy-enhancing-technology-pet-proposed-guide-on-synthetic-data-generation-by-pdpc/ Son erişim tarihi: 2 Haziran 2025.
• Rossum, H. van, & Borking, J. J. (1995). Privacy-enhancing Technologies: the path to anonymity. The Hague: Registratiekamer.
• Rubinstein, I. S. (2011). Regulating Privacy by Design.
• Berkeley Technology Law Journal, 26(3), 1409–1456.
• Rubinstein, I. S. (2018). Procedural, Institutional, Technical and Management Devices: A U.S. Perspective. I. Pernice & J. Pohle (Eds.), Privacy and Cyber Security on the Books and on the Ground. Berlin: Alexander von Humboldt Institute for Internet and Society.
• Srouji, J., & Mechler, T. (2020). How privacy-enhancing technologies are transforming privacy by design and default: Perspectives for today and tomorrow.
• Journal of Data Protection & Privacy, 3(3), 268–280.
• Szekely, B. (2022). Mitigating the Privacy Risks of AI through Privacy-Enhancing Technologies.
• Acta Universitatis Sapientiae: Legal Studies, 11(2), 35–64.
• The European Consumer Organization. (2020). Artificial Intelligence: what consumers say. https://www.beuc.eu/sites/default/files/publications/beuc-x-2020-078_artificial_intelligence_what_consumers_say_report.pdf Son erişim tarihi: 6 Haziran 2025. van Blarkom, G. W., et al. (2003). Chapter 3: PET.
• G. W. van Blarkom, J. J. Borking & J. G. E. Olk (Eds.), Handbook of Privacy and Privacy-Enhancing Technologies – The case of Intelligent Software Agents.
• The Hague: College bescherming persoonsgegevens. van Lieshout, M., et al. (2011). Privacy by Design: an alternative to existing practice in safeguarding privacy. Info, 13(6), 55–68.
• van Rest, J., et al. (2014). Designing Privacy-by-Design.
• B. Preneel & D. Ikonomou (Eds.), Privacy Technologies and Policy. London: Springer.