AAAI 2025technical0 citations
Privacy, Utility and Fairness: Navigating Trade-offs in Differentially Private Machine Learning
Abstract
Developing trustworthy AI requires advancing methods that meet key requirements such as privacy or fairness while maintaining strong utility, as well as understanding the intricate interdependencies between these dimensions, which often manifest as trade-offs. My PhD research focuses on differential privacy, which is widely regarded as the state-of-the-art for protecting privacy in data analysis and machine learning. I investigate the relationships between differential privacy, utility and fairness, with the goal of advancing the adoption of differentially private machine learning in real-world settings.
BibTeX
@article{Demelius_2025, title={Privacy, Utility and Fairness: Navigating Trade-offs in Differentially Private Machine Learning}, volume={39}, url={https://ojs.aaai.org/index.php/AAAI/article/view/35205}, DOI={10.1609/aaai.v39i28.35205}, abstractNote={Developing trustworthy AI requires advancing methods that meet key requirements such as privacy or fairness while maintaining strong utility, as well as understanding the intricate interdependencies between these dimensions, which often manifest as trade-offs. My PhD research focuses on differential privacy, which is widely regarded as the state-of-the-art for protecting privacy in data analysis and machine learning. I investigate the relationships between differential privacy, utility and fairness, with the goal of advancing the adoption of differentially private machine learning in real-world settings.}, number={28}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Demelius, Lea}, year={2025}, month={Apr.}, pages={29255-29256} }