Year: 2026
Type: article
Abstract: Generative Artificial Intelligence (GenAI) has evolved into a transformative technology whose unprecedented growth and public exposure have revealed challenging issues ranging from privacy protection to reducing factual inaccuracies and hallucinations, model security risks, legal complications, and a lack of interpretability. This position paper examines how Differential Privacy (DP), a mathematical privacy protection framework, can address both privacy concerns and other systemic challenges beyond privacy in GenAI. We argue that DP is a versatile and underutilized tool with significant potential to address many critical GenAI issues. To argue our claim, we connect the core principle of DP to these issues, evaluate existing research, and pose relevant research questions.
Source: Discover Artificial Intelligence
Authors: Jonas Kuntzer, Johannes Kaiser, Tamara T. Mueller, Anneliese Riess, Kristian Schwethelm, Georgios Kaissis, Daniel Rueckert
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,590
Funder: Technische Universität München
OpenAlex ID: W7165120980
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