Uniformity Testing in the Shuffle Model: Simpler, Better, Faster

• URL: http://arxiv.org/abs/2108.08987v1
• Date: Fri, 20 Aug 2021 03:43:12 GMT
• Title: Uniformity Testing in the Shuffle Model: Simpler, Better, Faster
• Authors: Cl\'ement L. Canonne and Hongyi Lyu
• Abstract summary: Uniformity testing, or testing whether independent observations are uniformly distributed, is the question in distribution testing. In this work, we considerably simplify the analysis of the known uniformity testing algorithm in the shuffle model.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Uniformity testing, or testing whether independent observations are uniformly distributed, is the prototypical question in distribution testing. Over the past years, a line of work has been focusing on uniformity testing under privacy constraints on the data, and obtained private and data-efficient algorithms under various privacy models such as central differential privacy (DP), local privacy (LDP), pan-privacy, and, very recently, the shuffle model of differential privacy. In this work, we considerably simplify the analysis of the known uniformity testing algorithm in the shuffle model, and, using a recent result on "privacy amplification via shuffling," provide an alternative algorithm attaining the same guarantees with an elementary and streamlined argument.

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