Privacy-Preserving Natural Language Dataset Generation
As we depend on data more heavily to power the insights made by machine learning systems, it becomes imperative that we design guarantees for protecting the privacy of such data. Recent research has shown the ease with which attacks such as membership inference or model inversion can extract potenti...
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| Format: | Thesis |
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Massachusetts Institute of Technology
2023
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| Online Access: | https://hdl.handle.net/1721.1/151313 |