2021/08/20 by Clément L. Canonne, Canonne, Clément L., Hongyi Lyu +1
Computer Science · #Adversarial Robustness in Machine Learning #Cryptography and Data Security #Cryptography and Security (cs.CR) #Data Structures and Algorithms (cs.DS) #Discrete Mathematics (cs.DM) #FOS: Computer and information sciences #Machine Learning (stat.ML) #Privacy-Preserving Technologies in Data
paper · pdf · doi:10.48550/arxiv.2108.08987
openalex publication_date 2021/08/20 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.