Distributed Differential Privacy via Mixnets

By Albert Cheu, Adam D. Smith, Jonathan Ullman, David Zeber, Maxim Zhilyaev. 2018

21 citations recorded by the source corpus.

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In Privacy & Security

Citations in the corpus

Secure Single-Server Aggregation with (Poly)Logarithmic Overhead (2020, methodology): “This primitive is the basis of the shuffle model of differential privacy [3, 4, 11, 19] and could be used for anonymized submissions [6].”
Privacy Amplification via Random Check-Ins (2020, methodology): “There is also a growing line of works on privacy amplification by shuffling [10, 19, 12, 4, 6, 22, 18] that focus on various ways in which protocols can be designed using trusted shuffling primitives.”
The Privacy Blanket of the Shuffle Model (2019, methodology): “Our amplification bound captures the regimes from both [13] and [10], thus providing a unified analysis of privacy amplification by shuffling for arbitrary local randomizers.”
Amplification by Shuffling: From Local to Central Differential Privacy via Anonymity (2018, background): “In concurrent and independent work, Cheu et al. [CSU + 18] have also examined an augmented local model of differential privacy that includes an anonymous channel.”
Encode, Shuffle, Analyze Privacy Revisited: Formalizations and Empirical Evaluation (2020, background): “Most recently, based on the Encode, Shuffle, Analyze (ESA) framework [2], a notable set of results has formally clarified how making reports anonymous can greatly improve privacy guarantees without loss of utility [3], [4].”
Consistent and Accurate Frequency Oracles under Local Differential Privacy (2019, background): “When many user reports are anonymized and then mixed (shuffled), one can argue a stronger privacy guarantee [8, 15].”
Locally Differentially Private Heavy Hitter Identification (2017, background): “reports are anonymized and then mixed (shuffled), one can argue a stronger privacy guarantee [9], [15].”
Locally Private Mean Estimation: Z-test and Tight Confidence Intervals (2018, unknown): “Note that this result was extended to other values of by Cheu et al. (2018).”
The Role of Interactivity in Local Differential Privacy (2019, unknown): “[11] prove their results for noninteractive protocols.”
Locally Private Gaussian Estimation (2018, background): “[9] Albert Cheu, Adam Smith, Jonathan Ullman, David Zeber, and Maxim Zhilyaev.”
On the Power of Multiple Anonymous Messages (2019, unknown): “8 As we discuss in Remark 1, generic reductions [29,20] showing that one can eﬃciently simulate an approximately diﬀerentially private protocol (i.e., with δ L ą 0) with a pure diﬀerentially private protocol (i.e., with δ L “ 0) are insuﬃcient to obtain tight lower bounds.”
Private Heavy Hitters and Range Queries in the Shuffled Model (2019, methodology): “When k ą 1 and α “ Ω p n q , the p α, β, k q -frequency oracle problem has also been referred to as that of (differentially private) selection [16, 44].”