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Prochlo: Strong Privacy for Analytics in the Crowd

arXiv 1710.00901 ↗First posted 2 Oct 2017cs.CRcited by 51

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Abstract (arXiv)

The large-scale monitoring of computer users' software activities has become commonplace, e.g., for application telemetry, error reporting, or demographic profiling. This paper describes a principled systems architecture---Encode, Shuffle, Analyze (ESA)---for performing such monitoring with high utility while also protecting user privacy. The ESA design, and its Prochlo implementation, are informed by our practical experiences with an existing, large deployment of privacy-preserving software monitoring. (cont.; see the paper)

Key facts. Cited by 51 papers in the map (data through 14 Oct 2026). First posted on arXiv 2 Oct 2017.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 24; 2022: 14; 2023: 10; 2024: 0; 2025: 2; 2026: 1.

51 papers.

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  1. 2021Federated Learning with Buffered Asynchronous Aggregationcited by 190
  2. 2023How to DP-fy ML: A Practical Guide to Machine Learning with Differential Privacycited by 153
  3. 2021The Distributed Discrete Gaussian Mechanism for Federated Learning with Secure Aggregationcited by 128
  4. 2021When the Curious Abandon Honesty: Federated Learning Is Not Privatecited by 127
  5. 2023Foundation Models and Fair Usecited by 116
  6. 2021On Large-Cohort Training for Federated Learningcited by 74
  7. 2022Composition of Differential Privacy & Privacy Amplification by Subsamplingcited by 45
  8. 2021Private Robust Estimation by Stabilizing Convex Relaxationscited by 38
  9. 2022Optimal Algorithms for Mean Estimation under Local Differential Privacycited by 36
  10. 2021Differentially Private Federated Bayesian Optimization with Distributed Explorationcited by 32
  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. 2022Shuffle Private Linear Contextual Banditscited by 27
  2. 2022Private Non-Convex Federated Learning Without a Trusted Servercited by 26
  3. 2022Almost Tight Error Bounds on Differentially Private Continual Countingcited by 26
  4. 2021The Internet of Federated Things (IoFT): A Vision for the Future and In-depth Survey of Data-driven Approaches for Federated Learningcited by 23
  5. 2021Dopamine: Differentially Private Federated Learning on Medical Datacited by 18
  6. 2021Shuffle Private Stochastic Convex Optimizationcited by 17
  7. 2021Privacy Amplification via Shuffling for Linear Contextual Banditscited by 17
  8. 2021On the Renyi Differential Privacy of the Shuffle Modelcited by 16
  9. 2023Reconstructing Individual Data Points in Federated Learning Hardened with Differential Privacy and Secure Aggregationcited by 16
  10. 2021Privately Learning Mixtures of Axis-Aligned Gaussianscited by 13
  11. 2022Private Convex Optimization in General Normscited by 13
  12. 2021Towards Sparse Federated Analytics: Location Heatmaps under Distributed Differential Privacy with Secure Aggregationcited by 10
  13. 2021User-Level Private Learning via Correlated Samplingcited by 9
  14. 2022Private Frequency Estimation via Projective Geometrycited by 9
  15. 2021Separation of Powers in Federated Learningcited by 8
  16. 2021Byzantine-robust Federated Learning through Spatial-temporal Analysis of Local Model Updatescited by 8
  17. 2021A Central Limit Theorem for Differentially Private Query Answeringcited by 7
  18. 2022Network Shuffling: Privacy Amplification via Random Walkscited by 6
  19. 2023Samplable Anonymous Aggregation for Private Federated Data Analysiscited by 6
  20. 2022Differential Secrecy for Distributed Data and Applications to Robust Differentially Secure Vector Summationcited by 5
  21. 2022Group privacy for personalized federated learningcited by 5
  22. 2022Hierarchical Federated Learning with Privacycited by 4
  23. 2022Shuffle Gaussian Mechanism for Differential Privacycited by 4
  24. 2023Differentially Private Multi-Site Treatment Effect Estimationcited by 4
  25. 2023(Private) Kernelized Bandits with Distributed Biased Feedbackcited by 3
  26. 2021Differentially-Private Sublinear-Time Clusteringcited by 2
  27. 2023Concurrent Shuffle Differential Privacy Under Continual Observationcited by 2
  28. 2021The Sample Complexity of Distribution-Free Parity Learning in the Robust Shuffle Modelcited by 1
  29. 2023Fingerprint Attack: Client De-Anonymization in Federated Learningcited by 1
  30. 2025SoK: Data Minimization in Machine Learningcited by 1
  31. 2025BinaryShield: Cross-Service Threat Intelligence in LLM Services using Privacy-Preserving Fingerprintscited by 1
  32. 2021A Shuffling Framework for Local Differential Privacycited by 0
  33. 2021Uniformity Testing in the Shuffle Model: Simpler, Better, Fastercited by 0
  34. 2021Infinitely Divisible Noise in the Low Privacy Regimecited by 0
  35. 2021Mitigating Leakage from Data Dependent Communications in Decentralized Computing using Differential Privacycited by 0
  36. 2022Privacy Amplification via Shuffled Check-Inscited by 0
  37. 2023When approximate design for fast homomorphic computation provides differential privacy guaranteescited by 0
  38. 2023Robust and differentially private stochastic linear banditscited by 0

50 most cited of 51 All 51 (JSON)