Private Federated Statistics in an Interactive Setting
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Abstract (arXiv)
Privately learning statistics of events on devices can enable improved user experience. Differentially private algorithms for such problems can benefit significantly from interactivity. We argue that an aggregation protocol can enable an interactive private federated statistics system where user's devices maintain control of the privacy assurance. We describe the architecture of such a system, and analyze its security properties.
Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). First posted on arXiv 18 Nov 2022.
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Citing papers per year: 2023: 4; 2024: 1.
5 papers.
- 2023One-Shot Federated Conformal Predictioncited by 27
- 2024SoK: A Review of Differentially Private Linear Models For High-Dimensional Datacited by 5
- 2023Differentially Private Heavy Hitter Detection using Federated Analyticscited by 2
- 2023Population Expansion for Training Language Models with Private Federated Learningcited by 1
- 2023Randomized algorithms for precise measurement of differentially-private, personalized recommendationscited by 1
- 2024SoK: A Review of Differentially Private Linear Models For High-Dimensional Data1 Apr 2024
- 2023Randomized algorithms for precise measurement of differentially-private, personalized recommendations7 Aug 2023
- 2023Differentially Private Heavy Hitter Detection using Federated Analytics21 Jul 2023
- 2023Population Expansion for Training Language Models with Private Federated Learning14 Jul 2023
- 2023One-Shot Federated Conformal Prediction13 Feb 2023