Beyond Differential Privacy: Synthetic Micro-Data Generation with Deep Generative Neural Networks
Recent advances in generative modeling, based on large scale deep neural networks, provide a novel approach for sharing individual-level datasets (micro-data) without privacy concerns. Unlike differential privacy, which enforces a specific query mechanism on data to ensure privacy, generative models...
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| Main Authors: | , |
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| Format: | Book Chapter |
| Language: | English |
| Published: |
IntechOpen
2020
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| Subjects: | |
| Online Access: | https://www.intechopen.com/chapters/72589 |
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| Summary: | Recent advances in generative modeling, based on large scale deep neural networks, provide a novel approach for sharing individual-level datasets (micro-data) without privacy concerns. Unlike differential privacy, which enforces a specific query mechanism on data to ensure privacy, generative models can accurately learn the statistical patterns of such micro-data and then be used to generate “synthetic data” that accurately reflects these statistical patterns, yet contain none of the original data itself, and thus can be safely shared for analysis and modeling without compromising privacy. The successful application of these techniques to various industries including healthcare, finance, and autonomous vehicles is promising and results in continued investment in research and development of generative models in both academia and industry. |
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| ISBN: | 978-1-83881-205-8 |
| DOI: | 10.5772/intechopen.92255 |