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Differentially Private Synthetic Data via Foundation Model APIs 1: Images
March 1, 2024, 5:44 a.m. | Zinan Lin, Sivakanth Gopi, Janardhan Kulkarni, Harsha Nori, Sergey Yekhanin
cs.LG updates on arXiv.org arxiv.org
Abstract: Generating differentially private (DP) synthetic data that closely resembles the original private data is a scalable way to mitigate privacy concerns in the current data-driven world. In contrast to current practices that train customized models for this task, we aim to generate DP Synthetic Data via APIs (DPSDA), where we treat foundation models as blackboxes and only utilize their inference APIs. Such API-based, training-free approaches are easier to deploy as exemplified by the recent surge …
abstract aim apis arxiv concerns contrast cs.cr cs.cv cs.lg current data data-driven foundation foundation model generate images practices privacy private data scalable synthetic synthetic data train type via world
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