Methodological Considerations for Anonymizing Tabular Data Using Generative Adversarial Networks

What is it about?

Data anonymization is a crucial process in data science, particularly when dealing with sensitive information subject to personal data protection laws. This paper explores using Generative Adversarial Networks (GANs) as an approach to anonymizing data. GANs utilize a Generator to create new data that resembles the original dataset, and a Discriminator to differentiate between real and generated data. The key phases of the GAN-based anonymization process include data preparation, generator and discriminator model design , adversarial training, synthetic data generation, and final data refinement. This approach allows for the creation of synthetic data that retains the statistical characteristics of the original dataset while ensuring individual privacy is protected. The paper provides technical details on the neural network architectures, activation functions, and training procedures that are critical to the success of this anonymization technique. By taking advantage of the capabilities of GANs, the interested parties can gain valuable insights from sensitive data without compromising individual privacy.

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Why is it important?

This paper explores using Generative Adversarial Networks (GANs) as an approach to anonymizing data. GANs utilize a Generator to create new data that resembles the original dataset, and a Discriminator to differentiate between real and generated data.