IEEE J Biomed Health Inform. 2024 Nov;28(11):6348-6359. doi: 10.1109/JBHI.2023.3287092. Epub 2024 Nov 6.
Although differential privacy metaverse data sharing can avoid privacy leakage of sensitive data, randomly perturbing local metaverse data will lead to an imbalance between utility and privacy. Therefore, this work proposed models and algorithms of differential privacy metaverse data sharing using Wasserstein generative adversarial networks (WGAN). Firstly, this study constructed the mathematical model of differential privacy metaverse data sharing by introducing appropriate regularization term related to generated data's discriminant probability into WGAN. Secondly, we established basic model and algorithm for differential privacy metaverse data sharing using WGAN based on the constructed mathematical model, and theoretically analyzed basic algorithm. Thirdly, we established federated model and algorithm for differential privacy metaverse data sharing using WGAN by serialized training based on basic model, and theoretically analyzed federated algorithm. Finally, based on utility and privacy metrics, we conducted a comparative analysis for the basic algorithm of differential privacy metaverse data sharing using WGAN, and experimental results validate theoretical results, which show that algorithms of differential privacy metaverse data sharing using WGAN maintaining equilibrium between privacy and utility.
虽然差分隐私元宇宙数据共享可以避免敏感数据的隐私泄露，但随机扰乱局部元宇宙数据会导致效用和隐私之间的失衡。因此，这项工作提出了使用 Wasserstein 生成对抗网络（WGAN）的差分隐私元宇宙数据共享模型和算法。首先，通过在 WGAN 中引入与生成数据判别概率相关的适当正则化项，构建了差分隐私元宇宙数据共享的数学模型。其次，基于所构建的数学模型，建立了基于 WGAN 的差分隐私元宇宙数据共享基本模型和算法，并从理论上分析了基本算法。然后，通过基于基本模型的序列化训练，建立了基于 WGAN 的联邦差分隐私元宇宙数据共享模型和算法，并从理论上分析了联邦算法。最后，基于效用和隐私指标，对基于 WGAN 的差分隐私元宇宙数据共享基本算法进行了对比分析，实验结果验证了理论结果，表明基于 WGAN 的差分隐私元宇宙数据共享算法能够在隐私和效用之间保持平衡。
