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

arXiv: https://arxiv.org/abs/1703.06255 · HTML: https://mlsanity.com/paper/1703.06255/

## Key facts

- Cited by 36 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Mar 2017.

## Built on by (most cited first, 30 of 36)

- [Stronger Privacy Amplification by Shuffling for R'enyi and Approximate Differential Privacy](https://mlsanity.com/paper/2208.04591/) (2022)
- [RoFL: Robustness of Secure Federated Learning](https://mlsanity.com/paper/2107.03311/) (2021)
- [Dopamine: Differentially Private Federated Learning on Medical Data](https://mlsanity.com/paper/2101.11693/) (2021)
- [Zero-Knowledge Proof-based Verifiable Decentralized Machine Learning in Communication Network: A Comprehensive Survey](https://mlsanity.com/paper/2310.14848/) (2023)
- [Flamingo: Multi-Round Single-Server Secure Aggregation with Applications to Private Federated Learning](https://mlsanity.com/paper/2308.09883/) (2023)
- [Federated Analytics: A survey](https://mlsanity.com/paper/2302.01326/) (2023)
- [ScionFL: Efficient and Robust Secure Quantized Aggregation](https://mlsanity.com/paper/2210.07376/) (2022)
- [zPROBE: Zero Peek Robustness Checks for Federated Learning](https://mlsanity.com/paper/2206.12100/) (2022)
- [Samplable Anonymous Aggregation for Private Federated Data Analysis](https://mlsanity.com/paper/2307.15017/) (2023)
- [Differential Secrecy for Distributed Data and Applications to Robust Differentially Secure Vector Summation](https://mlsanity.com/paper/2202.10618/) (2022)
- [Secure and Verifiable Data Collaboration with Low-Cost Zero-Knowledge Proofs](https://mlsanity.com/paper/2311.15310/) (2023)
- [Private Vector Mean Estimation in the Shuffle Model: Optimal Rates Require Many Messages](https://mlsanity.com/paper/2404.10201/) (2024)
- [Confidential Federated Computations](https://mlsanity.com/paper/2404.10764/) (2024)
- [Correlated Privacy Mechanisms for Differentially Private Distributed Mean Estimation](https://mlsanity.com/paper/2407.03289/) (2024)
- [PRECAD: Privacy-Preserving and Robust Federated Learning via Crypto-Aided Differential Privacy](https://mlsanity.com/paper/2110.11578/) (2021)
- [Masked LARk: Masked Learning, Aggregation and Reporting worKflow](https://mlsanity.com/paper/2110.14794/) (2021)
- [FuSeFL: Fully Secure and Scalable Federated Learning](https://mlsanity.com/paper/2507.13591/) (2025)
- [On the Security and Privacy of Federated Learning: A Survey with Attacks, Defenses, Frameworks, Applications, and Future Directions](https://mlsanity.com/paper/2508.13730/) (2025)
- [Randomized algorithms for precise measurement of differentially-private, personalized recommendations](https://mlsanity.com/paper/2308.03735/) (2023)
- [Balancing Privacy, Robustness, and Efficiency in Machine Learning](https://mlsanity.com/paper/2312.14712/) (2023)
- [PAPAYA Federated Analytics Stack: Engineering Privacy, Scalability and Practicality](https://mlsanity.com/paper/2412.02340/) (2024)
- [PREAMBLE: Private and Efficient Aggregation via Block Sparse Vectors](https://mlsanity.com/paper/2503.11897/) (2025)
- [Securing Private Federated Learning in a Malicious Setting: A Scalable TEE-Based Approach with Client Auditing](https://mlsanity.com/paper/2509.08709/) (2025)
- [SharedRequest: Privacy-Preserving Model-Agnostic Inference for Large Language Models](https://mlsanity.com/paper/2606.05004/) (2026)
- [SABLE: Secure And Byzantine robust LEarning](https://mlsanity.com/paper/2309.05395/) (2023)
- [Practical, Private Assurance of the Value of Collaboration via Fully Homomorphic Encryption](https://mlsanity.com/paper/2310.02563/) (2023)
- [Chu-ko-nu: A Reliable, Efficient, and Anonymously Authentication-Enabled Realization for Multi-Round Secure Aggregation in Federated Learning](https://mlsanity.com/paper/2402.15111/) (2024)
- [Fluent: Round-efficient Secure Aggregation for Private Federated Learning](https://mlsanity.com/paper/2403.06143/) (2024)
- [Unified Locational Differential Privacy Framework](https://mlsanity.com/paper/2405.03903/) (2024)
- [OmniLytics+: A Secure, Efficient, and Affordable Blockchain Data Market for Machine Learning through Off-Chain Processing](https://mlsanity.com/paper/2406.06477/) (2024)

## 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.