Beyond privacy: the potentialities of differential privacy in generative AI

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.