mlsanityData to 14 Oct 2026

Collecting Telemetry Data Privately

arXiv 1712.01524 ↗First posted 5 Dec 2017cs.CR cs.DScited by 149

Not on the map. Why?

Abstract (arXiv)

The collection and analysis of telemetry data from users' devices is routinely performed by many software companies. Telemetry collection leads to improved user experience but poses significant risks to users' privacy. Locally differentially private (LDP) algorithms have recently emerged as the main tool that allows data collectors to estimate various population statistics, while preserving privacy. The guarantees provided by such algorithms are typically very strong for a single round of telemetry collection, but degrade rapidly when telemetry is collected regularly. In particular, existing LDP algorithms are not suitable for repeated collection of counter data such as daily app usage statistics. In this paper, we develop new LDP mechanisms geared towards repeated collection of counter data, with formal privacy guarantees even after being executed for an arbitrarily long period of time. For two basic analytical tasks, mean estimation and histogram estimation, our LDP mechanisms for repeated data collection provide estimates with comparable or even the same accuracy as existing single-round LDP collection mechanisms. We conduct empirical evaluation on real-world counter datasets to verify our theoretical results. Our mechanisms have been deployed by Microsoft to collect telemetry across millions of devices.

Key facts. Cited by 149 papers in the map (data through 14 Oct 2026). First posted on arXiv 5 Dec 2017.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 30; 2022: 37; 2023: 24; 2024: 21; 2025: 25; 2026: 12.

149 papers.

Sort:

By area:

  1. 2021Deep Learning with Label Differential Privacycited by 117
  2. 2023Foundation Models and Fair Usecited by 116
  3. 2021Learning with User-Level Privacycited by 72
  4. 2022Privacy of Noisy Stochastic Gradient Descent: More Iterations without More Privacy Losscited by 61
  5. 2022Connect the Dots: Tighter Discrete Approximations of Privacy Loss Distributionscited by 52
  6. 2022Private Convex Optimization via Exponential Mechanismcited by 49
  7. 2021High Dimensional Differentially Private Stochastic Optimization with Heavy-tailed Datacited by 42
  8. 2021Federated Learning with Local Differential Privacy: Trade-offs between Privacy, Utility, and Communicationcited by 39
  9. 2021Private Robust Estimation by Stabilizing Convex Relaxationscited by 38
  10. 2021Generalized Linear Bandits with Local Differential Privacycited by 29
  11. 2022Stronger Privacy Amplification by Shuffling for R'enyi and Approximate Differential Privacycited by 29
  12. 2021Lossless Compression of Efficient Private Local Randomizerscited by 27
38 more
  1. 2021Differentially private inference via noisy optimizationcited by 27
  2. 2022A Critical Review on the Use (and Misuse) of Differential Privacy in Machine Learningcited by 27
  3. 2022Differentially Private Label Protection in Split Learningcited by 26
  4. 2022Optimal and Differentially Private Data Acquisition: Central and Local Mechanismscited by 24
  5. 2021The Internet of Federated Things (IoFT): A Vision for the Future and In-depth Survey of Data-driven Approaches for Federated Learningcited by 23
  6. 2022Differentially Private SGDA for Minimax Problemscited by 23
  7. 2022Learning to Generate Image Embeddings with User-level Differential Privacycited by 23
  8. 2021Differentially Private SGD with Non-Smooth Lossescited by 22
  9. 2021Locally Private k-Means in One Roundcited by 19
  10. 2021Dopamine: Differentially Private Federated Learning on Medical Datacited by 18
  11. 2021Private Prediction Setscited by 17
  12. 2021Wide Network Learning with Differential Privacycited by 17
  13. 2021The Role of Cross-Silo Federated Learning in Facilitating Data Sharing in the Agri-Food Sectorcited by 16
  14. 2021On the Renyi Differential Privacy of the Shuffle Modelcited by 16
  15. 2022Towards Private Learning on Decentralized Graphs with Local Differential Privacycited by 16
  16. 2022Faster Privacy Accounting via Evolving Discretizationcited by 15
  17. 2021Privately Learning Mixtures of Axis-Aligned Gaussianscited by 13
  18. 2022Private Convex Optimization in General Normscited by 13
  19. 2023Share Your Representation Only: Guaranteed Improvement of the Privacy-Utility Tradeoff in Federated Learningcited by 13
  20. 2021Faster Rates of Private Stochastic Convex Optimizationcited by 12
  21. 2022Private High-Dimensional Hypothesis Testingcited by 12
  22. 2023On the Efficacy of Differentially Private Few-shot Image Classificationcited by 12
  23. 2023User-Level Differential Privacy With Few Examples Per Usercited by 12
  24. 2024Why Does Differential Privacy with Large Epsilon Defend Against Practical Membership Inference Attacks?cited by 12
  25. 2022Protecting Global Properties of Datasets with Distribution Privacy Mechanismscited by 11
  26. 2024Optimal Differentially Private PCA and Estimation for Spiked Covariance Matricescited by 11
  27. 2024Optimal Federated Learning for Nonparametric Regression with Heterogeneous Distributed Differential Privacy Constraintscited by 10
  28. 2021User-Level Private Learning via Correlated Samplingcited by 9
  29. 2022Degree-Preserving Randomized Response for Graph Neural Networks under Local Differential Privacycited by 9
  30. 2022Fair NLP Models with Differentially Private Text Encoderscited by 9
  31. 2022Fine-grained Poisoning Attack to Local Differential Privacy Protocols for Mean and Variance Estimationcited by 9
  32. 2023Optimal Differentially Private Model Training with Public Datacited by 9
  33. 2024Analysis of Privacy Leakage in Federated Large Language Modelscited by 9
  34. 2021Frequency Estimation Under Multiparty Differential Privacy: One-shot and Streamingcited by 8
  35. 2024A Statistical Viewpoint on Differential Privacy: Hypothesis Testing, Representation and Blackwell's Theoremcited by 8
  36. 2024Federated Learning under Periodic Client Participation and Heterogeneous Data: A New Communication-Efficient Algorithm and Analysiscited by 8
  37. 2021Renyi Differential Privacy of the Subsampled Shuffle Model in Distributed Learningcited by 6
  38. 2021Distribution-Invariant Differential Privacycited by 6

50 most cited of 149 All 149 (JSON)