The rapid advancement of data-driven technologies and machine learning, particularly with the rise of Generative Artificial Intelligence (GenAI), has intensified and reshaped the global data economy. This evolving ecosystem is both enabled by and fuels the development of AI: vast datasets power intelligent systems, while AI technologies in turn create new demand for data generation, sharing, and monetization. Within this economy, data and machine learning models are increasingly treated as tradable digital assets that underpin innovation and value creation. However, this dynamic landscape raises critical challenges around data ownership, unauthorized redistribution, and secure participation. The ease of copying and transmitting digital assets at negligible cost complicates the enforcement of usage policies and the proof of rightful ownership, especially in the absence of robust technical safeguards. These challenges, if unaddressed, threaten the trust and agency of individuals and organizations that generate and rely on data within this ecosystem.

This thesis explores foundational technical mechanisms for asserting and verifying ownership rights over digital assets in a scalable and secure manner. It investigates: (1) how ownership information can be embedded across diverse data types; (2) how ownership can be proven publicly without disclosing underlying secrets; and (3) how contributors to collaboratively trained machine learning models can verifiably demonstrate participation without revealing sensitive information. By addressing these challenges, the thesis contributes to the technical foundations of a trustworthy and equitable data economy in which digital assets can be exchanged with confidence, and the ownership rights of data owners and users are reliably upheld

About Devris Isler

Devriş Isler is a PhD student at Data Transparency Group under the supervision of Prof. Nikolaos Laoutaris. Devriş received his MSc degree from Cryptography, Security, and Privacy Research Group, Koç University, İstanbul. His research interests include applied cryptography, privacy (e.g., data privacy, privacy perceptions) and usable security. Devriş is currently working on data ownership in data economy. He always enjoys solving security and privacy problems by taking advantage of cryptography (e.g., MPC, FHE, ZKP).

PhD Thesis Advisor: Prof. Nikolaos Laoutaris (IMDEA Networks Institute, Madrid, Spain)

University: Universidad Carlos III de Madrid (UC3M), Spain

Doctoral Program: Telematic Engineering

PhD Committee members:

•  President: Athina Markopoulou, University of California, Irvine
•  Secretary: Hamed Haddadi, Imperial College London & Brave
•  Panel member: Patricia Callejo Pinardo, Universidad Carlos III de Madrid