SecureML: A System for Scalable Privacy-Preserving Machine Learning
Abstract
Machine learning is widely used in practice to produce predictive models for applications such as image processing, speech and text recognition. These models are more accurate when trained on large amount of data collected from different sources. However, the massive data collection raises privacy concerns. In this paper, we present new and efficient protocols for privacy preserving machine learning for linear regression, logistic regression and neural network training using the stochastic gradient descent method. Our protocols fall in the two-server model where data owners distribute their private data among two non-colluding servers who train various models on the joint data using secure two-party computation (2PC). We develop new techniques to support secure arithmetic operations on shared decimal numbers, and propose MPC-friendly alternatives to non-linear functions such as sigmoid and softmax that are superior to prior work. We implement our system in C++. Our experiments validate that our protocols are several orders of magnitude faster than the state of the art implementations for privacy preserving linear and logistic regressions, and scale to millions of data samples with thousands of features. We also implement the first privacy preserving system for training neural networks.
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Cryptographic frameworks such as SecureML, CrypTen, and secure multi-party computation enable collaborative training without exposing raw data.
“Cryptographic frameworks such as SecureML (Mohassel & Zhang, 2017), CrypTen (Knott et al., 2021),and secure multi-party computation (Yao, 1982a), enable collaborative training without exposing raw data.”
From Open Challenges in Multi-Agent Security: Towards Secure Systems of Interacting AI Agents (2025) · cites this paper as Mohassel & Zhang, 2017
An extra layer of security added on top of clients' raw updates to avoid violating the property that no individual client's updates can be inspected before aggregation increases the amount of data to be uploaded.
“To avoid violating the property that no individual client's updates can be inspected before aggregation in the server, an extra layer of security is usually added on top of the clients' raw updates, further increasing the amount of data to be uploaded [17].”
From Vulnerabilities in federated learning (2021) · cites this paper as [17]
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- Authors
- Mohassel, P., & Zhang, Y.
- Affiliations
- Visa (United Kingdom) (GB)
- University of Maryland, College Park (US)
- ORCID
- Zhang, Yupeng
- Venue
- 2017 IEEE Symposium on Security and Privacy (SP)
- Publisher
- IEEE
- Published
- 2017-05
- Pages
- 19-38
- ISSN
- 1081-6011 (ISSN-L)
- OpenAlex
- W2701059868
- Cited by
- 1,936
- References
- 39
- Citations by year
- 2026: 102 · 2025: 268 · 2024: 302 · 2023: 278 · 2022: 233 · 2021: 260 · 2020: 248 · 2019: 152 · 2018: 84 · 2017: 9
- FWCI
- 61.49
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE Symposium on Security and Privacy (S&P) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Artificial Intelligence › Cryptography and Data Security
- Physical Sciences › Computer Science › Information Systems › Cloud Data Security Solutions
- Keywords
- privacy-preserving machine learning, secure multi-party computation, secure two-party computation, linear regression, logistic regression, neural network training, stochastic gradient descent
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