# BLAZE: Blazing Fast Privacy-Preserving Machine Learning

arXiv: https://arxiv.org/abs/2005.09042 · HTML: https://mlsanity.com/paper/2005.09042/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 May 2020.

## Built on by (most cited first, 30 of 32)

- [CrypTen: Secure Multi-Party Computation Meets Machine Learning](https://mlsanity.com/paper/2109.00984/) (2021)
- [CryptGPU: Fast Privacy-Preserving Machine Learning on the GPU](https://mlsanity.com/paper/2104.10949/) (2021)
- [A Survey of Trustworthy Federated Learning with Perspectives on Security, Robustness, and Privacy](https://mlsanity.com/paper/2302.10637/) (2023)
- [Sisyphus: A Cautionary Tale of Using Low-Degree Polynomial Activations in Privacy-Preserving Deep Learning](https://mlsanity.com/paper/2107.12342/) (2021)
- [Privacy-Preserving Video Classification with Convolutional Neural Networks](https://mlsanity.com/paper/2102.03513/) (2021)
- [SoK: Machine Learning Governance](https://mlsanity.com/paper/2109.10870/) (2021)
- [SecGNN: Privacy-Preserving Graph Neural Network Training and Inference as a Cloud Service](https://mlsanity.com/paper/2202.07835/) (2022)
- [Tetrad: Actively Secure 4PC for Secure Training and Inference](https://mlsanity.com/paper/2106.02850/) (2021)
- [ScionFL: Efficient and Robust Secure Quantized Aggregation](https://mlsanity.com/paper/2210.07376/) (2022)
- [Towards Secure and Practical Machine Learning via Secret Sharing and Random Permutation](https://mlsanity.com/paper/2108.07463/) (2021)
- [State-of-the-Art Approaches to Enhancing Privacy Preservation of Machine Learning Datasets: A Survey](https://mlsanity.com/paper/2404.16847/) (2024)
- [Adam in Private: Secure and Fast Training of Deep Neural Networks with Adaptive Moment Estimation](https://mlsanity.com/paper/2106.02203/) (2021)
- [SCOTCH: An Efficient Secure Computation Framework for Secure Aggregation](https://mlsanity.com/paper/2201.07730/) (2022)
- [Comet: Accelerating Private Inference for Large Language Model by Predicting Activation Sparsity](https://mlsanity.com/paper/2505.07239/) (2025)
- [SSNet: A Lightweight Multi-Party Computation Scheme for Practical Privacy-Preserving Machine Learning Service in the Cloud](https://mlsanity.com/paper/2406.02629/) (2024)
- [WW-FL: Secure and Private Large-Scale Federated Learning](https://mlsanity.com/paper/2302.09904/) (2023)
- [Approximating ReLU on a Reduced Ring for Efficient MPC-based Private Inference](https://mlsanity.com/paper/2309.04875/) (2023)
- [Wildest Dreams: Reproducible Research in Privacy-preserving Neural Network Training](https://mlsanity.com/paper/2403.03592/) (2024)
- [S++: A Fast and Deployable Secure-Computation Framework for Privacy-Preserving Neural Network Training](https://mlsanity.com/paper/2101.12078/) (2021)
- [ppAURORA: Privacy Preserving Area Under Receiver Operating Characteristic and Precision-Recall Curves](https://mlsanity.com/paper/2102.08788/) (2021)
- [MPClan: Protocol Suite for Privacy-Conscious Computations](https://mlsanity.com/paper/2206.12224/) (2022)
- [Pencil: Private and Extensible Collaborative Learning without the Non-Colluding Assumption](https://mlsanity.com/paper/2403.11166/) (2024)
- [PrivaDE: Privacy-preserving Data Evaluation for Blockchain-based Data Marketplaces](https://mlsanity.com/paper/2510.18109/) (2025)
- [CrypTorch: PyTorch-based Auto-tuning Compiler for Machine Learning with Multi-party Computation](https://mlsanity.com/paper/2511.19711/) (2025)
- [Morse-STF: Improved Protocols for Privacy-Preserving Machine Learning](https://mlsanity.com/paper/2109.11726/) (2021)
- [ABG: A Multi-Party Mixed Protocol Framework for Privacy-Preserving Cooperative Learning](https://mlsanity.com/paper/2202.02928/) (2022)
- [Threats, Vulnerabilities, and Controls of Machine Learning Based Systems: A Survey and Taxonomy](https://mlsanity.com/paper/2301.07474/) (2023)
- [Integrating Homomorphic Encryption and Trusted Execution Technology for Autonomous and Confidential Model Refining in Cloud](https://mlsanity.com/paper/2308.00963/) (2023)
- [Secure Multiparty Generative AI](https://mlsanity.com/paper/2409.19120/) (2024)
- [Protecting Confidentiality, Privacy and Integrity in Collaborative Learning](https://mlsanity.com/paper/2412.08534/) (2024)

## Abstract (arXiv)

Machine learning tools have illustrated their potential in many significant sectors such as healthcare and finance, to aide in deriving useful inferences. The sensitive and confidential nature of the data, in such sectors, raise natural concerns for the privacy of data. This motivated the area of Privacy-preserving Machine Learning (PPML) where privacy of the data is guaranteed. Typically, ML techniques require large computing power, which leads clients with limited infrastructure to rely on the method of Secure Outsourced Computation (SOC). In SOC setting, the computation is outsourced to a set of specialized and powerful cloud servers and the service is availed on a pay-per-use basis. In this work, we explore PPML techniques in the SOC setting for widely used ML algorithms-- Linear Regression, Logistic Regression, and Neural Networks. We propose BLAZE, a blazing fast PPML framework in the three server setting tolerating one malicious corruption over a ring (Zℓ). BLAZE achieves the stronger security guarantee of fairness (all honest servers get the output whenever the corrupt server obtains the same). Leveraging an input-independent preprocessing phase, BLAZE has a fast input-dependent online phase relying on efficient PPML primitives such as: (i) A dot product protocol for which the communication in the online phase is independent of the vector size, the first of its kind in the three server setting; (ii) A method for truncation that shuns evaluating expensive circuit for Ripple Carry Adders (RCA) and achieves a constant round complexity. This improves over the truncation method of ABY3 (Mohassel et al., CCS 2018) that uses RCA and consumes a round complexity that is of the order of the depth of RCA. An extensive benchmarking of BLAZE for the aforementioned ML algorithms over a 64-bit ring in both WAN and LAN settings shows massive improvements over ABY3.