Separating Local & Shuffled Differential Privacy via Histograms

DOI10.4230/lipics.itc.2020.1zbMath1525.94031arXiv1911.06879OpenAlexW3037148000MaRDI QIDQ6059355

Victor Balcer, Unnamed Author

Publication date: 2 November 2023

Full work available at URL: https://arxiv.org/abs/1911.06879

zbMATH Keywords

histogramsdifferential privacydistributed protocols

Mathematics Subject Classification ID

Cryptography (94A60) Information theory (general) (94A15)

Related Items (2)

Efficient noise generation to achieve differential privacy with applications to secure multiparty computation ⋮ On the power of multiple anonymous messages: frequency estimation and selection in the shuffle model of differential privacy

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