Dataset Usage Inference without Shadow Models or Held-out Data

2026-06-24 · Source: Machine Learning · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics, Cybersecurity & Data Privacy · Depth: Expert, quick

Summary

A new practical framework for Dataset Usage Inference (DUI) has been introduced, addressing critical limitations of existing methods. Current DUI approaches are impractical for modern large models and real data ownership disputes because they necessitate training expensive shadow models and require access to both known training samples and a confirmed in-distribution held-out set. This novel method eliminates these constraints by generating synthetic non-member samples, extracting diverse membership signals, and framing DUI as a mixture proportion estimation problem. Experiments on large image generative models demonstrate that this framework reliably quantifies dataset usage, offering data owners a practical tool to determine the extent of their data's contribution to model training.

Key takeaway

For data owners or legal teams navigating data ownership disputes, this new Dataset Usage Inference framework provides a crucial tool. You can now reliably quantify how much of your data was used to train a machine learning model, even large generative models, without the prohibitive costs of shadow models or the need for unavailable held-out data. This enables more accurate assessments of data contribution and strengthens your position in data rights discussions.

Key insights

A new DUI framework quantifies dataset usage in ML models without requiring shadow models or real held-out data.

Principles

• Existing DUI methods are impractical due to shadow model and held-out data reliance.
• Synthetic non-member samples can effectively substitute real held-out data for inference.
• Dataset usage inference can be modeled as a mixture proportion estimation problem.

Method

The method generates synthetic non-member samples, extracts diverse membership signals, and then casts Dataset Usage Inference as a mixture proportion estimation problem.

In practice

• Quantify dataset usage in large image generative models.
• Address real-world data ownership disputes.

Topics

• Dataset Usage Inference
• Membership Inference
• Machine Learning Models
• Data Ownership
• Generative Models
• Synthetic Data

Best for: Research Scientist, CTO, VP of Engineering/Data, AI Scientist, Machine Learning Engineer, AI Security Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Machine Learning.