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Discrete Distribution Estimation under Local Privacy

arXiv 1602.07387 ↗First posted 24 Feb 2016stat.ML cs.LGcited by 58

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Abstract (arXiv)

The collection and analysis of user data drives improvements in the app and web ecosystems, but comes with risks to privacy. This paper examines discrete distribution estimation under local privacy, a setting wherein service providers can learn the distribution of a categorical statistic of interest without collecting the underlying data. We present new mechanisms, including hashed K-ary Randomized Response (KRR), that empirically meet or exceed the utility of existing mechanisms at all privacy levels. New theoretical results demonstrate the order-optimality of KRR and the existing RAPPOR mechanism at different privacy regimes.

Key facts. Cited by 58 papers in the map (data through 7 Oct 2026). First posted on arXiv 24 Feb 2016.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 13; 2022: 17; 2023: 9; 2024: 8; 2025: 6; 2026: 5.

58 papers.

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  1. 2021A Field Guide to Federated Optimizationcited by 312
  2. 2021The Distributed Discrete Gaussian Mechanism for Federated Learning with Secure Aggregationcited by 127
  3. 2021Deep Learning with Label Differential Privacycited by 113
  4. 2022The Fundamental Price of Secure Aggregation in Differentially Private Federated Learningcited by 39
  5. 2021Lossless Compression of Efficient Private Local Randomizerscited by 27
  6. 2021Differentially Private Regret Minimization in Episodic Markov Decision Processescited by 23
  7. 2021Optimal Compression of Locally Differentially Private Mechanismscited by 22
  8. 2023Privacy Amplification via Compression: Achieving the Optimal Privacy-Accuracy-Communication Trade-off in Distributed Mean Estimationcited by 22
  9. 2023A Survey on Privacy in Graph Neural Networks: Attacks, Preservation, and Applicationscited by 22
  10. 2021Local Differential Privacy Is Equivalent to Contraction of Eγ-Divergencecited by 17
  11. 2022Privacy-Aware Compression for Federated Data Analysiscited by 17
  12. 2022Discrete Distribution Estimation under User-level Local Differential Privacycited by 17
38 more
  1. 2021On the Renyi Differential Privacy of the Shuffle Modelcited by 16
  2. 2022Private Graph Extraction via Feature Explanationscited by 16
  3. 2021Privately Learning Mixtures of Axis-Aligned Gaussianscited by 13
  4. 2022You Are What You Write: Preserving Privacy in the Era of Large Language Modelscited by 11
  5. 2022XRand: Differentially Private Defense against Explanation-Guided Attackscited by 11
  6. 2021Towards Sparse Federated Analytics: Location Heatmaps under Distributed Differential Privacy with Secure Aggregationcited by 10
  7. 2022Private Frequency Estimation via Projective Geometrycited by 9
  8. 2022SoK: Differential Privacy on Graph-Structured Datacited by 9
  9. 2023Federated Analytics: A surveycited by 9
  10. 2022Contraction of Locally Differentially Private Mechanismscited by 8
  11. 2022Degree-Preserving Randomized Response for Graph Neural Networks under Local Differential Privacycited by 7
  12. 2021Renyi Differential Privacy of the Subsampled Shuffle Model in Distributed Learningcited by 6
  13. 2022Nonparametric extensions of randomized response for private confidence setscited by 6
  14. 2024A Systematic and Formal Study of the Impact of Local Differential Privacy on Fairness: Preliminary Resultscited by 6
  15. 2021Robust Testing and Estimation under Manipulation Attackscited by 5
  16. 2022Robust Estimation of Discrete Distributions under Local Differential Privacycited by 5
  17. 2022Group privacy for personalized federated learningcited by 5
  18. 2023(Local) Differential Privacy has NO Disparate Impact on Fairnesscited by 5
  19. 2024Locally Differentially Private In-Context Learningcited by 5
  20. 2022Algorithms with More Granular Differential Privacy Guaranteescited by 4
  21. 2023On the Impact of Multi-dimensional Local Differential Privacy on Fairnesscited by 4
  22. 2026Token-Efficient Change Detection in LLM APIscited by 4
  23. 2024Locally Private Estimation with Public Featurescited by 3
  24. 2024Locally Private Sampling with Public Datacited by 3
  25. 2023Multi-Message Shuffled Privacy in Federated Learningcited by 2
  26. 2024Enhancing Learning with Label Differential Privacy by Vector Approximationcited by 2
  27. 2024Private Estimation when Data and Privacy Demands are Correlatedcited by 2
  28. 2025Privacy-Preserving Conformal Prediction Under Local Differential Privacycited by 2
  29. 2022Locally Differentially Private Distributed Deep Learning via Knowledge Distillationcited by 1
  30. 2022One-bit Submission for Locally Private Quasi-MLE: Its Asymptotic Normality and Limitationcited by 1
  31. 2023Locally Differentially Private Distributed Online Learning with Guaranteed Optimalitycited by 1
  32. 2023On the Utility Gain of Iterative Bayesian Update for Locally Differentially Private Mechanismscited by 1
  33. 2024Better Locally Private Sparse Estimation Given Multiple Samples Per Usercited by 1
  34. 2025Learning from End User Data with Shuffled Differential Privacy over Kernel Densitiescited by 1
  35. 2025Devil's Hand: Data Poisoning Attacks to Locally Private Graph Learning Protocolscited by 1
  36. 2025Consistent Estimation of Numerical Distributions under Local Differential Privacy by Wavelet Expansioncited by 1
  37. 2021Partial sensitivity analysis in differential privacycited by 0
  38. 2022The Poisson binomial mechanism for secure and private federated learningcited by 0

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