{
  "id": 5884,
  "url": "https://arxiv.org/abs/2604.12459v1",
  "title": "Operationalising the Right to be Forgotten in LLMs: A Lightweight Sequential Unlearning Framework for Privacy-Aligned Deployment in Politically Sensitive Environments",
  "summary": "Large Language Models (LLMs) are increasingly deployed in politically sensitive environments, where memorisation of personal data or confidential content raises regulatory concerns under frameworks such as the GDPR and its Right to be Forgotten. Translating such legal principles into large-scale generative systems presents significant technical challenges. We introduce a lightweight sequential unlearning framework that explicitly separates retention and suppression objectives. The method first s",
  "authors": "Esen Kurt, Haithem Afli",
  "category": "research",
  "topics": "regulation,privacy-surveillance,environment",
  "orgs": null,
  "regions": "eu",
  "published_at": "2026-04-14T08:45:12.000Z",
  "fetched_at": "2026-07-14T16:32:06.466Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/5884",
  "original_url": "https://arxiv.org/abs/2604.12459v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}