{
  "id": 12983,
  "url": "https://arxiv.org/abs/2607.19580v1",
  "title": "End-to-End Differential Privacy in Training Deep Neural Network Classifiers",
  "summary": "Differentially private machine learning enables model training on sensitive data while ensuring that individual data is unlikely to be recoverable from the parameters of the resulting model. However, existing work often privatizes both training inputs and their labels, and these protections may be conservative when labels are public or can be safely made public. Therefore, in this work we propose a novel private training framework that instead privatizes training inputs while keeping labels publ",
  "authors": "Huaiyuan Rao, Calvin Hawkins, Alexander Benvenuti, Matthew Hale",
  "category": "research",
  "topics": "privacy-surveillance",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-21T21:15:55.000Z",
  "fetched_at": "2026-07-23T05:10:49.458Z",
  "source_slug": "x-arxiv-cs-cr-ai-security",
  "source_name": "arXiv cs.CR (AI security)",
  "source_homepage": "https://arxiv.org/list/cs.CR/recent",
  "ethics_ai_record_url": "https://ethics.ai/record/12983",
  "original_url": "https://arxiv.org/abs/2607.19580v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}