Private aggregation from fewer anonymous messages

DOI10.1007/978-3-030-45724-2_27zbMath1492.94111arXiv1909.11073OpenAlexW3031144431MaRDI QIDQ2119033

Rasmus Pagh, Ameya Velingker, Pasin Manurangsi, Badih Ghazi

Publication date: 23 March 2022

Full work available at URL: https://arxiv.org/abs/1909.11073

zbMATH Keywords

differential privacyanonymous channelsecure aggregationshuffled model

Mathematics Subject Classification ID

Cryptography (94A60) Privacy of data (68P27)

Related Items (1)

Additive randomized encodings and their applications

Cites Work

• On the power of multiple anonymous messages: frequency estimation and selection in the shuffle model of differential privacy
• Distributed differential privacy via shuffling
• The privacy blanket of the shuffle model
• Synopses for Massive Data: Samples, Histograms, Wavelets, Sketches
• Computational Advertising: Techniques for Targeting Relevant Ads
• Efficient noise-tolerant learning from statistical queries
• Advances and Open Problems in Federated Learning
• Amplification by Shuffling: From Local to Central Differential Privacy via Anonymity

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