Private Wasserstein Distance with Random Noises
<|reference_start|>Private Wasserstein Distance with Random Noises: Wasserstein distance is a principle measure of data divergence from a distributional standpoint. However, its application becomes challenging in the context of data privacy, where sharing raw data is restricted. Prior attempts have employed techniques like Differential Privacy or Federated optimization to approximate Wasserstein distance. Nevertheless, these approaches often lack accuracy and robustness against potential attack. In this study, we investigate the underlying triangular properties within the Wasserstein space, leading to a straightforward solution named TriangleWad. This approach enables the computation of Wasserstein distance between datasets stored across different entities. Notably, TriangleWad is 20 times faster, making raw data information truly invisible, enhancing resilience against attacks, and without sacrificing estimation accuracy. Through comprehensive experimentation across various tasks involving both image and text data, we demonstrate its superior performance and generalizations.<|reference_end|>
@article{li2024private,
    title={Private Wasserstein Distance with Random Noises},
    author={Wenqian Li, Haozhi Wang, Zhe Huang, Yan Pang},
    journal={arXiv preprint arXiv:2404.06787},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.06787},
    primaryClass={cs.LG cs.AI}
}