Regular submission
An original or work-in-progress paper of up to four pages, excluding references and appendices.
Machine Learning/CORE Unranked
InfPriv 2026
(Fast Track) Beyond Private Training: The New Landscape of AI Privacy
InfPriv at NeurIPS 2026 focuses on privacy beyond model training, including inference-time defenses, agentic privacy guardrails, private synthetic data, and using AI systems to enforce or verify privacy guarantees.
Paper fit
Contribution paths
A strong submission should clearly identify its contribution and evaluate it appropriately.
NeurIPS fast track
A relevant NeurIPS 2026 submission accompanied by its reviews, metareviews, rebuttal, and discussion.
Research areas in scope
01
Post-training privacy
Privacy for agentic systemsInference-only private synthesisPrivacy in in-context learningAI for privacyPrivacy benchmarks and auditing
Policies worth checking twice
- Regular submissions use the NeurIPS template and undergo strict double-blind review.
- Fabricated references, AI-generated citations, or significant unsupported claims trigger automatic rejection.
- Fast-track submissions must append their complete NeurIPS 2026 review record.
Official sources
Compiled from the official call for papers. The organizers’ pages remain authoritative.
Last verified September 9, 2026