{
  "id": 13727,
  "url": "https://arxiv.org/abs/2607.21895v1",
  "title": "PrivDNN: A Secure Multi-Party Computation Framework for Deep Learning using Partial DNN Encryption",
  "summary": "In the past decade, we have witnessed an exponential growth of deep learning models, platforms, and applications. While existing DL applications and Machine Learning as a service (MLaaS) frameworks assume fully trusted models, the need for privacy-preserving DNN evaluation arises. In a secure multi-party computation scenario, both the model and the data are considered proprietary, i.e., the model owner does not want to reveal the highly valuable DL model to the user, while the user does not wish",
  "authors": "Liangqin Ren, Zeyan Liu, Fengjun Li, Kaitai Liang, Zhu Li, Bo Luo",
  "category": "research",
  "topics": "privacy-surveillance",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-24T01:52:18.000Z",
  "fetched_at": "2026-07-27T05:10:06.638Z",
  "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/13727",
  "original_url": "https://arxiv.org/abs/2607.21895v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}