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Algorithms with More Granular Differential Privacy Guarantees. (arXiv:2209.04053v1 [cs.CR])
Sept. 12, 2022, 1:20 a.m. | Badih Ghazi, Ravi Kumar, Pasin Manurangsi, Thomas Steinke
cs.CR updates on arXiv.org arxiv.org
Differential privacy is often applied with a privacy parameter that is larger
than the theory suggests is ideal; various informal justifications for
tolerating large privacy parameters have been proposed. In this work, we
consider partial differential privacy (DP), which allows quantifying the
privacy guarantee on a per-attribute basis. In this framework, we study several
basic data analysis and learning tasks, and design algorithms whose
per-attribute privacy parameter is smaller that the best possible privacy
parameter for the entire record of …
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