Privacy as a Free Lunch: Crafting Initial Distilled Datasets through the Kaleidoscope

Shuo Shi, Peng Sun, Xinyi Shang, Tianyu Du, Xuhong Zhang, Jianwei Yin, Tao Lin

Keywords: Dataset Distillation

Abstract:

The advancement of deep learning necessitates stringent data privacy guarantees. Dataset distillation has shown potential in preserving differential privacy while maintaining training efficiency. This study first identifies that data generated by state-of-the-art dataset distillation methods strongly resembles to real data, indicating severe privacy leakage. We define this phenomenon as explicit privacy leakage. We theoretically analyze that although distilled datasets can ensure differential privacy to some extent, a high \IPC can weaken both differential privacy and explicit privacy. Furthermore, we reveal that the primary source of privacy leakage in distilled data stems from the common approach of initializing distilled images as real data. To address this, we propose a plug-and-play module, Kaleidoscopic Transformation (KT), designed to introduce enhanced strong perturbations to the selected real data during the initialization phase. Extensive experiments demonstrate that our method ensures both differential privacy and explicit privacy, while preserving the generalization performance of the distilled data. Our code will be publicly available.

Primary Area: unsupervised, self-supervised, semi-supervised, and supervised representation learning

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Submission Number: 10266