{
  "id": 6151,
  "url": "https://arxiv.org/abs/2604.07884v1",
  "title": "Reinforcement-Guided Synthetic Data Generation for Privacy-Sensitive Identity Recognition",
  "summary": "High-fidelity generative models are increasingly needed in privacy-sensitive scenarios, where access to data is severely restricted due to regulatory and copyright constraints. This scarcity hampers model development--ironically, in settings where generative models are most needed to compensate for the lack of data. This creates a self-reinforcing challenge: limited data leads to poor generative models, which in turn fail to mitigate data scarcity. To break this cycle, we propose a reinforcement",
  "authors": "Xuemei Jia, Jiawei Du, Hui Wei, Jun Chen, Joey Tianyi Zhou, Zheng Wang",
  "category": "research",
  "topics": "regulation,privacy-surveillance,copyright-ip",
  "orgs": null,
  "regions": null,
  "published_at": "2026-04-09T06:52:03.000Z",
  "fetched_at": "2026-07-14T16:32:15.638Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/6151",
  "original_url": "https://arxiv.org/abs/2604.07884v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}