{
  "id": 14494,
  "url": "https://arxiv.org/abs/2607.23649v1",
  "title": "DP-IVON-Gradsq: Differentially Private Squared-Gradient Improved Variational Online Newton",
  "summary": "Differential privacy provides formal privacy guarantees for training neural networks on sensitive data, while Bayesian deep learning offers a principled framework for uncertainty-aware prediction. Combining these two objectives remains challenging, as privacy noise can interact with the stochasticity introduced by Bayesian posterior sampling. In this work, we investigate differentially private variational Bayesian learning through the Improved Variational Online Newton (IVON) optimizer. We intro",
  "authors": "Nour Jamoussi, Ikram Dridi, Giuseppe Serra, Marios Kountouris",
  "category": "research",
  "topics": "privacy-surveillance,finance-investment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-26T13:32:49.000Z",
  "fetched_at": "2026-07-29T05:10:12.205Z",
  "source_slug": "arxiv-cslg",
  "source_name": "arXiv cs.LG",
  "source_homepage": "https://arxiv.org/list/cs.LG/recent",
  "ethics_ai_record_url": "https://ethics.ai/record/14494",
  "original_url": "https://arxiv.org/abs/2607.23649v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}