PriLOM 2026

Deadlines
Machine Learning/CORE Unranked

PriLOM 2026

Privacy in the Era of Large Opaque Models: Theoretical, Legal, and Practical Perspectives

1784325600000Paris, FranceOfficial conference site Site reachable

PriLOM 2026 is a NeurIPS workshop focused on privacy risks in large opaque models and agentic systems, examining how memorization, data leakage, and system opacity challenge traditional privacy definitions and mitigation strategies. It brings together researchers from machine learning, law, HCI, and privacy to explore measurable risks, governance frameworks, and societal impacts of AI systems that act on sensitive information.

Official CFP Back to deadlines Verified September 9, 2026

Paper fit

Contribution paths

A strong submission should clearly identify its contribution and evaluate it appropriately.

Short Papers

Technical research papers up to 4 pages (excluding references and appendices), following NeurIPS 2026 formatting guidelines and using the workshop's LaTeX template. Must present original, unpublished work, though arXiv preprints are permitted.

Position Papers

Well-reasoned perspectives on challenges, opportunities, or future directions in privacy for opaque AI systems. Must present a compelling argument with evidence and engagement with related work, without requiring completed research. Evaluated on reasoning quality and potential to stimulate discussion.

Research areas in scope

01

Topics of Interest

Privacy definitions for foundation models and agentic systems, including contextual, legal, social, and formal perspectives.Memorization, leakage, and privacy attacks on models, including extraction, inference, reconstruction, prompt injection, cross-session leakage, and multi-agent risks.Benchmarks, audits, and realistic evaluations of privacy risks, memorization, and safeguard.Mitigations and formal protections, including access control, unlearning, privacy-preserving tool use, differential privacy, and verification.Trade-offs, governance, and societal impacts of privacy-sensitive AI, including utility, safety, transparency, compliance, and downstream harms.

Policies worth checking twice

  • Submissions must be original and unpublished, though arXiv preprints are allowed.
  • All submissions must follow NeurIPS 2026 formatting guidelines and the workshop’s LaTeX template; non-compliant submissions may be rejected without review.
  • AI-generated or fabricated references, citations, or results will result in rejection without review.
  • Authors must verify the accuracy and validity of all content, even when using generative AI tools.
  • Use of LLMs or agents for important, original, or non-standard tasks must be documented in the appendix or main text.
  • Spell checkers, grammar tools, and basic code assistance do not need to be disclosed.
  • LLMs or agents cannot be listed as authors.
  • Prompt injections or attempts to manipulate the review process are strictly prohibited.

Official sources

Compiled from the official call for papers. The organizers’ pages remain authoritative.

Official conference siteCall for Papers[Download LaTeX Template]

Last verified September 9, 2026