The Role of Interactivity in Local Differential Privacy
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Abstract (arXiv)
We study the power of interactivity in local differential privacy. First, we focus on the difference between fully interactive and sequentially interactive protocols. Sequentially interactive protocols may query users adaptively in sequence, but they cannot return to previously queried users. The vast majority of existing lower bounds for local differential privacy apply only to sequentially interactive protocols, and before this paper it was not known whether fully interactive protocols were more powerful. We resolve this question. First, we classify locally private protocols by their compositionality, the multiplicative factor k ≥ 1 by which the sum of a protocol's single-round privacy parameters exceeds its overall privacy guarantee. We then show how to efficiently transform any fully interactive k-compositional protocol into an equivalent sequentially interactive protocol with an O(k) blowup in sample complexity. Next, we show that our reduction is tight by exhibiting a family of problems such that for any k, there is a fully interactive k-compositional protocol which solves the problem, while no sequentially interactive protocol can solve the problem without at least an tilde Omega(k) factor more examples. We then turn our attention to hypothesis testing problems. We show that for a large class of compound hypothesis testing problems --- which include all simple hypothesis testing problems as a special case --- a simple noninteractive test is optimal among the class of all (possibly fully interactive) tests.
Key facts. Cited by 15 papers in the map (data through 14 Oct 2026). First posted on arXiv 7 Apr 2019.
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Citing papers per year: 2021: 4; 2022: 4; 2023: 4; 2024: 0; 2025: 2; 2026: 1.
15 papers.
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- 2021Private Federated Learning Without a Trusted Server: Optimal Algorithms for Convex Lossescited by 30
- 2022Private Non-Convex Federated Learning Without a Trusted Servercited by 26
- 2021Shuffle Private Stochastic Convex Optimizationcited by 17
- 2023Simple Binary Hypothesis Testing under Local Differential Privacy and Communication Constraintscited by 10
- 2022Contraction of Locally Differentially Private Mechanismscited by 8
- 2022The Role of Interactivity in Structured Estimationcited by 7
- 2021Task-aware Privacy Preservation for Multi-dimensional Datacited by 5
- 2022Transfer Learning In Differential Privacy's Hybrid-Modelcited by 3
- 2023Sample-Optimal Locally Private Hypothesis Selection and the Provable Benefits of Interactivitycited by 3
- 2023Private and Communication-Efficient Algorithms for Entropy Estimationcited by 2
- 2023Differentially Private Heavy Hitter Detection using Federated Analyticscited by 2
- 2025Federated Learning of Quantile Inference under Local Differential Privacycited by 2
- 2026SLDP: Semi-Local Differential Privacy for Density-Adaptive Analytics21 Feb 2026
- 2025Federated Learning of Quantile Inference under Local Differential Privacy26 Sep 2025
- 2025Query-Efficient Locally Private Hypothesis Selection via the Scheffe Graph19 Sep 2025
- 2023Sample-Optimal Locally Private Hypothesis Selection and the Provable Benefits of Interactivity9 Dec 2023
- 2023Differentially Private Heavy Hitter Detection using Federated Analytics21 Jul 2023
- 2023Private and Communication-Efficient Algorithms for Entropy Estimation12 May 2023
- 2023Simple Binary Hypothesis Testing under Local Differential Privacy and Communication Constraints9 Jan 2023
- 2022Contraction of Locally Differentially Private Mechanisms24 Oct 2022
- 2022The Role of Interactivity in Structured Estimation14 Mar 2022
- 2022Private Non-Convex Federated Learning Without a Trusted Server13 Mar 2022
- 2022Transfer Learning In Differential Privacy's Hybrid-Model28 Jan 2022
- 2021Task-aware Privacy Preservation for Multi-dimensional Data5 Oct 2021
- 2021Private Federated Learning Without a Trusted Server: Optimal Algorithms for Convex Lossescited by 30
- 2022Private Non-Convex Federated Learning Without a Trusted Servercited by 26
- 2021Shuffle Private Stochastic Convex Optimizationcited by 17
- 2023Simple Binary Hypothesis Testing under Local Differential Privacy and Communication Constraintscited by 10
- 2022Contraction of Locally Differentially Private Mechanismscited by 8
- 2022The Role of Interactivity in Structured Estimationcited by 7
- 2021Task-aware Privacy Preservation for Multi-dimensional Datacited by 5
- 2022Transfer Learning In Differential Privacy's Hybrid-Modelcited by 3
- 2023Sample-Optimal Locally Private Hypothesis Selection and the Provable Benefits of Interactivitycited by 3
- 2023Private and Communication-Efficient Algorithms for Entropy Estimationcited by 2
- 2023Differentially Private Heavy Hitter Detection using Federated Analyticscited by 2
- 2025Federated Learning of Quantile Inference under Local Differential Privacycited by 2
12 most cited of 15 All 15 (JSON)
- 2026SLDP: Semi-Local Differential Privacy for Density-Adaptive Analytics21 Feb 2026
- 2025Federated Learning of Quantile Inference under Local Differential Privacy26 Sep 2025
- 2025Query-Efficient Locally Private Hypothesis Selection via the Scheffe Graph19 Sep 2025
- 2023Sample-Optimal Locally Private Hypothesis Selection and the Provable Benefits of Interactivity9 Dec 2023
- 2023Differentially Private Heavy Hitter Detection using Federated Analytics21 Jul 2023
- 2023Private and Communication-Efficient Algorithms for Entropy Estimation12 May 2023
- 2023Simple Binary Hypothesis Testing under Local Differential Privacy and Communication Constraints9 Jan 2023
- 2022Contraction of Locally Differentially Private Mechanisms24 Oct 2022
- 2022The Role of Interactivity in Structured Estimation14 Mar 2022
- 2022Private Non-Convex Federated Learning Without a Trusted Server13 Mar 2022
- 2022Transfer Learning In Differential Privacy's Hybrid-Model28 Jan 2022
- 2021Task-aware Privacy Preservation for Multi-dimensional Data5 Oct 2021
12 newest of 15 All 15 (JSON)