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RAPPOR: Randomized Aggregatable Privacy-Preserving Ordinal Response

arXiv 1407.6981 ↗First posted 25 Jul 2014cs.CRcited by 272

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Abstract (arXiv)

Randomized Aggregatable Privacy-Preserving Ordinal Response, or RAPPOR, is a technology for crowdsourcing statistics from end-user client software, anonymously, with strong privacy guarantees. In short, RAPPORs allow the forest of client data to be studied, without permitting the possibility of looking at individual trees. By applying randomized response in a novel manner, RAPPOR provides the mechanisms for such collection as well as for efficient, high-utility analysis of the collected data. In particular, RAPPOR permits statistics to be collected on the population of client-side strings with strong privacy guarantees for each client, and without linkability of their reports. This paper describes and motivates RAPPOR, details its differential-privacy and utility guarantees, discusses its practical deployment and properties in the face of different attack models, and, finally, gives results of its application to both synthetic and real-world data.

Key facts. Cited by 272 papers in the map (data through 14 Oct 2026). First posted on arXiv 25 Jul 2014.

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Citing papers per year: 2021: 48; 2022: 55; 2023: 56; 2024: 43; 2025: 41; 2026: 29.

272 papers.

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  1. 2021Trustworthy AI: From Principles to Practicescited by 207
  2. 2021Deep Learning with Label Differential Privacycited by 117
  3. 2023Foundation Models and Fair Usecited by 116
  4. 2021Optimal Accounting of Differential Privacy via Characteristic Functioncited by 86
  5. 2021Robust and Differentially Private Mean Estimationcited by 66
  6. 2023Locally Differentially Private Document Generation Using Zero Shot Promptingcited by 63
  7. 2022Privacy of Noisy Stochastic Gradient Descent: More Iterations without More Privacy Losscited by 61
  8. 2021A Theory of PAC Learnability of Partial Concept Classescited by 60
  9. 2021Socially Responsible AI Algorithms: Issues, Purposes, and Challengescited by 56
  10. 2021Federated Social Recommendation with Graph Neural Networkcited by 56
  11. 2021Antipodes of Label Differential Privacy: PATE and ALIBIcited by 52
  12. 2022Connect the Dots: Tighter Discrete Approximations of Privacy Loss Distributionscited by 52
38 more
  1. 2022Private Convex Optimization via Exponential Mechanismcited by 49
  2. 2021Covariance-Aware Private Mean Estimation Without Private Covariance Estimationcited by 46
  3. 2021Private Robust Estimation by Stabilizing Convex Relaxationscited by 38
  4. 2022Optimal Algorithms for Mean Estimation under Local Differential Privacycited by 36
  5. 2021Decision Making with Differential Privacy under a Fairness Lenscited by 32
  6. 2021Generalized Linear Bandits with Local Differential Privacycited by 29
  7. 2022Stronger Privacy Amplification by Shuffling for R'enyi and Approximate Differential Privacycited by 29
  8. 2021Lossless Compression of Efficient Private Local Randomizerscited by 27
  9. 2021Differentially private inference via noisy optimizationcited by 27
  10. 2022A Critical Review on the Use (and Misuse) of Differential Privacy in Machine Learningcited by 27
  11. 2023From Robustness to Privacy and Backcited by 27
  12. 2021Stronger Privacy for Federated Collaborative Filtering with Implicit Feedbackcited by 26
  13. 2022Optimal and Differentially Private Data Acquisition: Central and Local Mechanismscited by 24
  14. 2022DP-PCA: Statistically Optimal and Differentially Private PCAcited by 24
  15. 2022Differentially Private SGDA for Minimax Problemscited by 23
  16. 2021Differentially Private SGD with Non-Smooth Lossescited by 22
  17. 2021Optimal Compression of Locally Differentially Private Mechanismscited by 22
  18. 2021Label differential privacy via clusteringcited by 20
  19. 2021Locally Private k-Means in One Roundcited by 19
  20. 2021Dopamine: Differentially Private Federated Learning on Medical Datacited by 18
  21. 2021Private Prediction Setscited by 17
  22. 2021Wide Network Learning with Differential Privacycited by 17
  23. 2021Privacy Amplification via Shuffling for Linear Contextual Banditscited by 17
  24. 2022Privacy-Aware Compression for Federated Data Analysiscited by 17
  25. 2022Discrete Distribution Estimation under User-level Local Differential Privacycited by 17
  26. 2023ReSQueing Parallel and Private Stochastic Convex Optimizationcited by 17
  27. 2023Active Membership Inference Attack under Local Differential Privacy in Federated Learningcited by 17
  28. 2023Randomized Quantization is All You Need for Differential Privacy in Federated Learningcited by 17
  29. 2021On the Renyi Differential Privacy of the Shuffle Modelcited by 16
  30. 2022Faster Privacy Accounting via Evolving Discretizationcited by 15
  31. 2022Differentially Private Estimation of Heterogeneous Causal Effectscited by 14
  32. 2022DarKnight: An Accelerated Framework for Privacy and Integrity Preserving Deep Learning Using Trusted Hardwarecited by 14
  33. 2024Trustworthy Distributed AI Systems: Robustness, Privacy, and Governancecited by 14
  34. 2021Differentially Private Imaging via Latent Space Manipulationcited by 13
  35. 2021Privately Learning Mixtures of Axis-Aligned Gaussianscited by 13
  36. 2022Private Convex Optimization in General Normscited by 13
  37. 2023Share Your Representation Only: Guaranteed Improvement of the Privacy-Utility Tradeoff in Federated Learningcited by 13
  38. 2022Private High-Dimensional Hypothesis Testingcited by 12

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