mlsanityData to 14 Oct 2026

Towards Sparse Federated Analytics: Location Heatmaps under Distributed Differential Privacy with Secure Aggregation

arXiv 2111.02356 ↗First posted 3 Nov 2021cs.CR cs.LGcited by 10

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Abstract (arXiv)

We design a scalable algorithm to privately generate location heatmaps over decentralized data from millions of user devices. It aims to ensure differential privacy before data becomes visible to a service provider while maintaining high data accuracy and minimizing resource consumption on users' devices. To achieve this, we revisit distributed differential privacy based on recent results in secure multiparty computation, and we design a scalable and adaptive distributed differential privacy approach for location analytics. Evaluation on public location datasets shows that this approach successfully generates metropolitan-scale heatmaps from millions of user samples with a worst-case client communication overhead that is significantly smaller than existing state-of-the-art private protocols of similar accuracy.

Key facts. Cited by 10 papers in the map (data through 14 Oct 2026). Builds on 31 of its 76 references that are in the map. First posted on arXiv 3 Nov 2021.

Builds on

31 of 76 references.

References · 31

  1. 2019Advances and Open Problems in Federated Learningcited by 3,046
  2. 2018Improving the Gaussian Mechanism for Differential Privacy: Analytical Calibration and Optimal Denoisingcited by 218
  3. 2018A Hybrid Approach to Privacy-Preserving Federated Learningcited by 210
  4. 2016Practical Secure Aggregation for Federated Learning on User-Held Datacited by 178
  5. 2018Amplification by Shuffling: From Local to Central Differential Privacy via Anonymitycited by 164
  6. 2021The Distributed Discrete Gaussian Mechanism for Federated Learning with Secure Aggregationcited by 128
  7. 2018Distributed Differential Privacy via Shufflingcited by 115
  8. 2020Hiding Among the Clones: A Simple and Nearly Optimal Analysis of Privacy Amplification by Shufflingcited by 96
  9. 2019The Privacy Blanket of the Shuffle Modelcited by 74
  10. 2021The Skellam Mechanism for Differentially Private Federated Learningcited by 68
  11. 2020FastSecAgg: Scalable Secure Aggregation for Privacy-Preserving Federated Learningcited by 61
  12. 2016Discrete Distribution Estimation under Local Privacycited by 59
19 more
  1. 2017Prochlo: Strong Privacy for Analytics in the Crowdcited by 51
  2. 2020Encode, Shuffle, Analyze Privacy Revisited: Formalizations and Empirical Evaluationcited by 39
  3. 2020Private Summation in the Multi-Message Shuffle Modelcited by 34
  4. 2016Privacy Odometers and Filters: Pay-as-you-Go Compositioncited by 33
  5. 2019Federated Heavy Hitters Discovery with Differential Privacycited by 32
  6. 2017Optimal Schemes for Discrete Distribution Estimation under Locally Differential Privacycited by 29
  7. 2017Practical Locally Private Heavy Hitterscited by 29
  8. 2016PrivTree: A Differentially Private Algorithm for Hierarchical Decompositionscited by 22
  9. 2017Heavy Hitters and the Structure of Local Privacycited by 20
  10. 2019Separating Local & Shuffled Differential Privacy via Histogramscited by 16
  11. 2021Private Counting from Anonymous Messages: Near-Optimal Accuracy with Vanishing Communication Overheadcited by 16
  12. 2019Private Aggregation from Fewer Anonymous Messagescited by 15
  13. 2020Pure Differentially Private Summation from Anonymous Messagescited by 15
  14. 2020Shuffled Model of Federated Learning: Privacy, Communication and Accuracy Trade-offscited by 15
  15. 2020Connecting Robust Shuffle Privacy and Pan-Privacycited by 12
  16. 2017Marginal Release Under Local Differential Privacycited by 9
  17. 2017A Framework for Evaluating Epidemic Forecastscited by 8
  18. 2021Frequency Estimation Under Multiparty Differential Privacy: One-shot and Streamingcited by 8
  19. 2017Computational Differential Privacy from Lattice-based Cryptographycited by 6

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 1; 2022: 2; 2023: 1; 2024: 3; 2025: 2; 2026: 1.

10 papers.

  1. 2024PrE-Text: Training Language Models on Private Federated Data in the Age of LLMscited by 29
  2. 2022Federated Select: A Primitive for Communication- and Memory-Efficient Federated Learningcited by 11
  3. 2022FedWalk: Communication Efficient Federated Unsupervised Node Embedding with Differential Privacycited by 9
  4. 2021Location Leakage in Federated Signal Mapscited by 2
  5. 2023Differentially Private Heavy Hitter Detection using Federated Analyticscited by 2
  6. 2024PAPAYA Federated Analytics Stack: Engineering Privacy, Scalability and Practicalitycited by 1
  7. 2025How to Securely Shuffle? A survey about Secure Shufflers for privacy-preserving computationscited by 1
  8. 2024Federated Computing -- Survey on Building Blocks, Extensions and Systemscited by 0
  9. 2025DP-Hype: Federated Differentially Private Hyperparameter Searchcited by 0
  10. 2026TAPAS: Efficient Two-Server Asymmetric Private Aggregation Beyond Prio(+)cited by 0