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Privacy Guarantees in Synthetic Data

In: Synthetic Data for Deep Learning

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  • Sergey I. Nikolenko

    (Synthesis AI
    Steklov Institute of Mathematics)

Abstract

In this chapter, we discuss another important field of applications for synthetic data: ensuring privacy. In many real-world problems, real data is sensitive enough that it is impossible to release. One possible solution could be to train generative models that would produce new synthetic datasets based on real data, while the real data itself would remain secret. But how can we be sure that real data will not be inadvertently leaked? Guarantees in this regard can be provided by the framework of differential privacy. We give a brief introduction to differential privacy, its relation to machine learning, and the guarantees that it can provide for synthetic data generation.

Suggested Citation

  • Sergey I. Nikolenko, 2021. "Privacy Guarantees in Synthetic Data," Springer Optimization and Its Applications, in: Synthetic Data for Deep Learning, chapter 0, pages 269-283, Springer.
    • Handle: RePEc:spr:spochp:978-3-030-75178-4_11
    DOI: 10.1007/978-3-030-75178-4_11
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