mlsanityData to 7 Oct 2026

Prio: Private, Robust, and Scalable Computation of Aggregate Statistics

arXiv 1703.06255 ↗First posted 18 Mar 2017cs.CRcited by 35

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Abstract (arXiv)

This paper presents Prio, a privacy-preserving system for the collection of aggregate statistics. Each Prio client holds a private data value (e.g., its current location), and a small set of servers compute statistical functions over the values of all clients (e.g., the most popular location). As long as at least one server is honest, the Prio servers learn nearly nothing about the clients' private data, except what they can infer from the aggregate statistics that the system computes. To protect functionality in the face of faulty or malicious clients, Prio uses secret-shared non-interactive proofs (SNIPs), a new cryptographic technique that yields a hundred-fold performance improvement over conventional zero-knowledge approaches. Prio extends classic private aggregation techniques to enable the collection of a large class of useful statistics. For example, Prio can perform a least-squares regression on high-dimensional client-provided data without ever seeing the data in the clear.

Key facts. Cited by 35 papers in the map (data through 7 Oct 2026). First posted on arXiv 18 Mar 2017.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 4; 2022: 4; 2023: 9; 2024: 9; 2025: 5; 2026: 4.

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  1. 2022Stronger Privacy Amplification by Shuffling for R'enyi and Approximate Differential Privacycited by 29
  2. 2021RoFL: Robustness of Secure Federated Learningcited by 23
  3. 2021Dopamine: Differentially Private Federated Learning on Medical Datacited by 18
  4. 2023Zero-Knowledge Proof-based Verifiable Decentralized Machine Learning in Communication Network: A Comprehensive Surveycited by 18
  5. 2023Flamingo: Multi-Round Single-Server Secure Aggregation with Applications to Private Federated Learningcited by 16
  6. 2023Federated Analytics: A surveycited by 9
  7. 2022ScionFL: Efficient and Robust Secure Quantized Aggregationcited by 8
  8. 2022zPROBE: Zero Peek Robustness Checks for Federated Learningcited by 6
  9. 2023Samplable Anonymous Aggregation for Private Federated Data Analysiscited by 6
  10. 2022Differential Secrecy for Distributed Data and Applications to Robust Differentially Secure Vector Summationcited by 5
  11. 2024Private Vector Mean Estimation in the Shuffle Model: Optimal Rates Require Many Messagescited by 5
  12. 2023Secure and Verifiable Data Collaboration with Low-Cost Zero-Knowledge Proofscited by 4
23 more
  1. 2024Confidential Federated Computationscited by 4
  2. 2024Correlated Privacy Mechanisms for Differentially Private Distributed Mean Estimationcited by 4
  3. 2021PRECAD: Privacy-Preserving and Robust Federated Learning via Crypto-Aided Differential Privacycited by 3
  4. 2021Masked LARk: Masked Learning, Aggregation and Reporting worKflowcited by 2
  5. 2025On the Security and Privacy of Federated Learning: A Survey with Attacks, Defenses, Frameworks, Applications, and Future Directionscited by 2
  6. 2023Randomized algorithms for precise measurement of differentially-private, personalized recommendationscited by 1
  7. 2023Balancing Privacy, Robustness, and Efficiency in Machine Learningcited by 1
  8. 2024PAPAYA Federated Analytics Stack: Engineering Privacy, Scalability and Practicalitycited by 1
  9. 2025PREAMBLE: Private and Efficient Aggregation via Block Sparse Vectorscited by 1
  10. 2025FuSeFL: Fully Secure and Scalable Federated Learningcited by 1
  11. 2023SABLE: Secure And Byzantine robust LEarningcited by 0
  12. 2023Practical, Private Assurance of the Value of Collaboration via Fully Homomorphic Encryptioncited by 0
  13. 2024Chu-ko-nu: A Reliable, Efficient, and Anonymously Authentication-Enabled Realization for Multi-Round Secure Aggregation in Federated Learningcited by 0
  14. 2024Fluent: Round-efficient Secure Aggregation for Private Federated Learningcited by 0
  15. 2024Unified Locational Differential Privacy Frameworkcited by 0
  16. 2024OmniLytics+: A Secure, Efficient, and Affordable Blockchain Data Market for Machine Learning through Off-Chain Processingcited by 0
  17. 2024Federated and differentially private estimation of KL divergencecited by 0
  18. 2025Securing Private Federated Learning in a Malicious Setting: A Scalable TEE-Based Approach with Client Auditingcited by 0
  19. 2025Refereed Learningcited by 0
  20. 2026TAPAS: Efficient Two-Server Asymmetric Private Aggregation Beyond Prio(+)cited by 0
  21. 2026SharedRequest: Privacy-Preserving Model-Agnostic Inference for Large Language Modelscited by 0
  22. 2026PRoVeFL: Private Robust and Verifiable Aggregation in Federated Learningcited by 0
  23. 2026Progressive-Resolution Secure Aggregation for Federated Learningcited by 0