AI RESEARCH
Evaluating LLM Simulators as Differentially Private Data Generators
arXiv CS.LG
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ArXi:2604.15461v1 Announce Type: new LLM-based simulators offer a promising path for generating complex synthetic data where traditional differentially private (DP) methods struggle with high-dimensional user profiles. But can LLMs faithfully reproduce statistical distributions from DP-protected inputs? We evaluate this using PersonaLedger, an agentic financial simulator, seeded with DP synthetic personas derived from real user statistics.