Privacy Guarantees in Synthetic Data

Sergey I. Nikolenko ()
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Sergey I. Nikolenko: Synthesis AI

Chapter Chapter 11 in Synthetic Data for Deep Learning, 2021, pp 269-283 from Springer

Abstract: 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.

Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:spr:spochp:978-3-030-75178-4_11

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DOI: 10.1007/978-3-030-75178-4_11

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