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arXiv · 2603.01053

Turning Black Box into White Box: Dataset Distillation Leaks

Huajie Chen·Tianqing Zhu·Yuchen Zhong·Yang Zhang·Shang Wang·Feng He·Lefeng Zhang·Jialiang Shen·Minghao Wang·Wanlei Zhou
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Abstract

Dataset distillation compresses a large real dataset into a small synthetic one, enabling models trained on the synthetic data to achieve performance comparable to those trained on the real data. Although synthetic datasets are assumed to be privacy-preserving, we show that existing distillation methods can cause severe privacy leakage because synthetic datasets implicitly encode the weight trajectories of the distilled model, they become over-informative and exploitable by adversaries. To expose this risk, we introduce the Information Revelation Attack (IRA) against state-of-the-art distillation techniques. Experiments show that IRA accurately predicts both the distillation algorithm and model architecture, and can successfully infer membership and recover sensitive samples from the real dataset.

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Huajie Chen, Tianqing Zhu, Yuchen Zhong, Yang Zhang, Shang Wang, Feng He, Lefeng Zhang, Jialiang Shen, Minghao Wang, Wanlei Zhou. 2026-03-01. Turning Black Box into White Box: Dataset Distillation Leaks. https://arxiv.org/abs/2603.01053

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