Iterative Methods for Private Synthetic Data: Unifying Framework and New Methods

We study private synthetic data generation for query release, where the goal is to construct a sanitized version of a sensitive dataset, subject to differential privacy, that approximately preserves the answers to a large collection of statistical queries. We first present an algorithmic framework t...

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Veröffentlicht in:	arXiv.org 2021-12
Hauptverfasser:	Liu, Terrance, Vietri, Giuseppe, Wu, Zhiwei Steven
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Iterative algorithms Iterative methods Neural networks Optimization Privacy Synthetic data
Online-Zugang:	Volltext
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