Watermarking for Proprietary Dataset Protection

2026-06-30

AI Summary

This work addresses the challenge of training data membership inference and the difficulty of protecting proprietary data in modern language models by systematically introducing output watermarking into membership inference tasks. The authors propose embedding traceable “radioactive” watermarks into a subset of training samples, enabling the model’s outputs to retain identifiable source signatures. Experimental results demonstrate that, under high exposure rates of the training subset, the proposed method achieves membership detection performance comparable to conventional loss-based baselines. These findings validate the feasibility and effectiveness of leveraging watermarking mechanisms not only for data copyright protection but also for reliable membership inference, offering a novel direction for safeguarding sensitive training data in large language models.

Abstract

A growing body of literature suggests that training data membership inference problems are fundamentally hard tasks in modern language modeling settings. We argue that output watermarking techniques are the right gadget to make training membership tests for generative models more tractable, based on prior results showing that language models exhibit residual watermark "radioactivity" under partially watermarked training datasets. We pit a watermark-based dataset inference approach head-to-head against traditional loss-based membership inference methods and show that watermarking can achieve comparable membership detection performance when subset exposure is high enough, under an alternate set of assumptions.

Problem

• watermarking
• membership inference
• proprietary dataset
• generative models
• language modeling

Innovation

• output watermarking
• membership inference
• language models
• dataset protection
• watermark radioactivity