# SWIFT: Super-fast and Robust Privacy-Preserving Machine Learning

arXiv: https://arxiv.org/abs/2005.10296 · HTML: https://mlsanity.com/paper/2005.10296/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 May 2020.

## Built on by (most cited first, 20 of 20)

- [A Survey of Trustworthy Federated Learning with Perspectives on Security, Robustness, and Privacy](https://mlsanity.com/paper/2302.10637/) (2023)
- [Privacy-Preserving Video Classification with Convolutional Neural Networks](https://mlsanity.com/paper/2102.03513/) (2021)
- [ezDPS: An Efficient and Zero-Knowledge Machine Learning Inference Pipeline](https://mlsanity.com/paper/2212.05428/) (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)
- [MUDGUARD: Taming Malicious Majorities in Federated Learning using Privacy-Preserving Byzantine-Robust Clustering](https://mlsanity.com/paper/2208.10161/) (2022)
- [Adam in Private: Secure and Fast Training of Deep Neural Networks with Adaptive Moment Estimation](https://mlsanity.com/paper/2106.02203/) (2021)
- [FedComm: Federated Learning as a Medium for Covert Communication](https://mlsanity.com/paper/2201.08786/) (2022)
- [Preserving Data Privacy for ML-driven Applications in Open Radio Access Networks](https://mlsanity.com/paper/2402.09710/) (2024)
- [Wildest Dreams: Reproducible Research in Privacy-preserving Neural Network Training](https://mlsanity.com/paper/2403.03592/) (2024)
- [MPClan: Protocol Suite for Privacy-Conscious Computations](https://mlsanity.com/paper/2206.12224/) (2022)
- [PrivaDE: Privacy-preserving Data Evaluation for Blockchain-based Data Marketplaces](https://mlsanity.com/paper/2510.18109/) (2025)
- [On the Evaluation of User Privacy in Deep Neural Networks using Timing Side Channel](https://mlsanity.com/paper/2208.01113/) (2022)
- [Integrating Homomorphic Encryption and Trusted Execution Technology for Autonomous and Confidential Model Refining in Cloud](https://mlsanity.com/paper/2308.00963/) (2023)
- [Privacy-Preserving Verifiable Neural Network Inference Service](https://mlsanity.com/paper/2411.07468/) (2024)
- [EVA-S2PLoR: Decentralized Secure 2-party Logistic Regression with A Subtly Hadamard Product Protocol (Full Version)](https://mlsanity.com/paper/2501.05223/) (2025)
- [From Local Patterns to Global Understanding: Cross-Stock Trend Integration for Enhanced Predictive Modeling](https://mlsanity.com/paper/2505.16573/) (2025)
- [Secure Sparse Matrix Multiplications and their Applications to Privacy-Preserving Machine Learning](https://mlsanity.com/paper/2510.14894/) (2025)
- [LRD-MPC: Efficient MPC Inference through Low-rank Decomposition](https://mlsanity.com/paper/2602.14397/) (2026)

## Abstract (arXiv)

Performing machine learning (ML) computation on private data while maintaining data privacy, aka Privacy-preserving Machine Learning~(PPML), is an emergent field of research. Recently, PPML has seen a visible shift towards the adoption of the Secure Outsourced Computation~(SOC) paradigm due to the heavy computation that it entails. In the SOC paradigm, computation is outsourced to a set of powerful and specially equipped servers that provide service on a pay-per-use basis. In this work, we propose SWIFT, a robust PPML framework for a range of ML algorithms in SOC setting, that guarantees output delivery to the users irrespective of any adversarial behaviour. Robustness, a highly desirable feature, evokes user participation without the fear of denial of service. At the heart of our framework lies a highly-efficient, maliciously-secure, three-party computation (3PC) over rings that provides guaranteed output delivery (GOD) in the honest-majority setting. To the best of our knowledge, SWIFT is the first robust and efficient PPML framework in the 3PC setting. SWIFT is as fast as (and is strictly better in some cases than) the best-known 3PC framework BLAZE (Patra et al. NDSS'20), which only achieves fairness. We extend our 3PC framework for four parties (4PC). In this regime, SWIFT is as fast as the best known fair 4PC framework Trident (Chaudhari et al. NDSS'20) and twice faster than the best-known robust 4PC framework FLASH (Byali et al. PETS'20). We demonstrate our framework's practical relevance by benchmarking popular ML algorithms such as Logistic Regression and deep Neural Networks such as VGG16 and LeNet, both over a 64-bit ring in a WAN setting. For deep NN, our results testify to our claims that we provide improved security guarantee while incurring no additional overhead for 3PC and obtaining 2x improvement for 4PC.