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Provable Privacy with Non-Private Pre-Processing
March 21, 2024, 4:10 a.m. | Yaxi Hu, Amartya Sanyal, Bernhard Sch\"olkopf
cs.CR updates on arXiv.org arxiv.org
Abstract: When analysing Differentially Private (DP) machine learning pipelines, the potential privacy cost of data-dependent pre-processing is frequently overlooked in privacy accounting. In this work, we propose a general framework to evaluate the additional privacy cost incurred by non-private data-dependent pre-processing algorithms. Our framework establishes upper bounds on the overall privacy guarantees by utilising two new technical notions: a variant of DP termed Smooth DP and the bounded sensitivity of the pre-processing algorithms. In addition to …
accounting algorithms arxiv cost cs.ai cs.cr cs.lg data framework general machine machine learning non pipelines privacy private private data stat.ml work
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