The paper introduces a novel approach to training GANs on locally privatized user data, showcasing the effectiveness of entropic optimal transport in mitigating privacy noise and facilitating convergence. Experimental results demonstrate the efficacy of the proposed method in learning from privatized data.
Local differential privacy is discussed as a powerful method for privacy-preserving data collection, emphasizing the importance of rethinking machine learning methods for accurate model extraction from noisy, privatized data. The study explores training generative models from locally privatized user data, focusing on the problem of learning accurate population-level models from noisy samples. The paper highlights the significance of entropic regularization of optimal transport in enabling GANs to recover original distributions from privatized samples efficiently.
The research provides theoretical insights into convergence guarantees for locally differential private frameworks with entropic optimal transport, showcasing how this approach uniquely mitigates privacy noise and dimensionality challenges. Empirical validation supports the theoretical contributions, demonstrating superior performance in practical scenarios.
Ke Bahasa Lain
dari konten sumber
arxiv.org
Pertanyaan yang Lebih Dalam