Synthetic Data Generation Models for Privacy-Safe AI

  • Authors

    • Per Brinch Hansen Professor of Computer Science, Syracuse University, Denmark Author
    • Borge Diderichsen Professor, Technical University of Denmark, Denmark Author

    DOI:

    https://doi.org/10.67228/30715628/IJMIET-2024PII3P3B

    Published 09-05-2024

  • Synthetic Data, Privacy-Preserving AI, Generative Adversarial Networks, Variational Autoencoders, Diffusion Models, Differential Privacy, Artificial Intelligence, Machine Learning, Data Privacy, Data Security

    Issue

    Vol. 7 No. 2 (2024)

    Section

    Articles

    How to Cite

    [1]
    P. B. Hansen and B. Diderichsen, “Synthetic Data Generation Models for Privacy-Safe AI”, ijmiet, vol. 7, no. 2, pp. 01–19, Sep. 2024, doi: 10.67228/30715628/IJMIET-2024PII3P3B.

  • Abstract

    The increasing demand for high-quality datasets for AI development has raised significant privacy, security, and regulatory concerns, particularly in sensitive domains such as healthcare, finance, and government. Synthetic data generation addresses these challenges by creating artificial datasets that preserve the statistical characteristics of real data while protecting individual privacy. Recent advances in generative AI, including GANs, VAEs, diffusion models, and transformer-based models, have significantly improved the realism and utility of synthetic data. This paper surveys synthetic data generation techniques, privacy-preserving methods, applications, research challenges, and future directions for developing trustworthy AI systems.

  • References

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