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Privacy-Utility Data Generation with GANs by dzn20010108 is a document available to read on EtoBox.

What is Privacy-Utility Data Generation with GANs about?

This paper presents models and algorithms for privacy-utility equilibrium data generation using Wasserstein generative adversarial networks (WGAN) to address the conflict between local data sharing and privacy protection. It constructs a basic mathematical model and algorithms that ensure the generated data is computationally indistinguishable from real data, thus maintaining privacy while allowing for effective data sharing. The study also develops a federated model that utilizes serialized training method

Author

dzn20010108

Language

EN