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arXiv Machine Learning By Keiyu Nosaka, Yamato Suetake, Yuichi Takano, Yukihiko Okada, Akiko Yoshise

Geometric Data Perturbation with Noisy-Anchor Alignment for Privacy-Preserving Collaborative Learning

Read the original on arXiv Machine Learning →

Geometric Data Perturbation (GDP) allows participants to share distance‑preserving transformations of their private data for one‑shot collaborative learning. The paper examines the vulnerability when an analyst colludes with participants, showing that shared‑anchor alignment can restore compatibility but also enables exact data recovery. To mitigate this, the authors propose adding noise to the anchor representations rather than the private data, demonstrating through experiments on MNIST and CelebA that this approach yields better privacy‑utility trade‑offs under collusion.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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