Trident: Efficient 4PC Framework for Privacy Preserving Machine Learning (1912.02631v2)

Abstract: Machine learning has started to be deployed in fields such as healthcare and finance, which propelled the need for and growth of privacy-preserving machine learning (PPML). We propose an actively secure four-party protocol (4PC), and a framework for PPML, showcasing its applications on four of the most widely-known machine learning algorithms -- Linear Regression, Logistic Regression, Neural Networks, and Convolutional Neural Networks. Our 4PC protocol tolerating at most one malicious corruption is practically efficient as compared to the existing works. We use the protocol to build an efficient mixed-world framework (Trident) to switch between the Arithmetic, Boolean, and Garbled worlds. Our framework operates in the offline-online paradigm over rings and is instantiated in an outsourced setting for machine learning. Also, we propose conversions especially relevant to privacy-preserving machine learning. The highlights of our framework include using a minimal number of expensive circuits overall as compared to ABY3. This can be seen in our technique for truncation, which does not affect the online cost of multiplication and removes the need for any circuits in the offline phase. Our B2A conversion has an improvement of $\mathbf{7} \times$ in rounds and $\mathbf{18} \times$ in the communication complexity. The practicality of our framework is argued through improvements in the benchmarking of the aforementioned algorithms when compared with ABY3. All the protocols are implemented over a 64-bit ring in both LAN and WAN settings. Our improvements go up to $\mathbf{187} \times$ for the training phase and $\mathbf{158} \times$ for the prediction phase when observed over LAN and WAN.

• The paper introduces a novel 4-party computation protocol that enhances efficiency and security in privacy-preserving machine learning by reducing online communication overhead by 25%.
• The framework leverages a mixed computational model that transitions between arithmetic, Boolean, and garbled circuits to streamline function evaluations and boost throughput.
• Empirical results demonstrate up to 251.84x improvement in training iteration efficiency and significant prediction throughput gains across various machine learning benchmarks.

An Analysis of Trident: Efficient Four-Party Computation Framework for Privacy Preserving Machine Learning

The paper entitled "Trident: Efficient 4PC Framework for Privacy Preserving Machine Learning" introduces a novel protocol that addresses the challenges of privacy-preserving machine learning (PPML) using a four-party computation (4PC) scheme. This research is particularly vital in contexts such as healthcare and finance, where sensitive data necessitates strict confidentiality.

Technical Contributions

This paper provides a systematic framework that enhances the efficiency and security of PPML by capitalizing on a four-party (4PC) scenario. Unlike previous three-party computational models, this work improves communication complexity and computational efficacy under the assumption of at most one party being maliciously corrupted. Each of the four parties plays a critical role, providing not just redundancy but also allowing for innovative protocol design that reduces online communication overhead by 25% compared to existing solutions and operates on 64-bit integer rings.

Key highlights include:

• Protocol Enhancements: Compared to the protocol from Gordon et al. (ASIACRYPT 2018), Trident reduces the online phase's reliance on the fourth party, which is pertinent only during input sharing and output reconstruction. This distinction translates to a substantial reduction in operational requirements, leading to optimized communication efficiency.
• Mixed World Framework: It shifts gracefully between arithmetic, Boolean, and garbled computational models. These transitions are crucial as they enable more effective calculations than those possible in single-model computations, which are often suboptimal in handling complex operations necessary for machine learning models. This mixed framework ensures high throughput in the online phase.
• Truncation and Conversion Improvements: Innovative protocols for function evaluations eliminate the need for costly circuits in truncation. Conversion between Boolean to arithmetic representations is both simplified and accelerated, achieving an impressive 7x improvement in rounds and 18x in communication complexity.

Empirical Validation

The paper demonstrates the practicality of the Trident framework through the implementation of benchmark machine learning algorithms, such as linear regression, logistic regression, neural networks (NN), and convolutional neural networks (CNN). Results indicate significant improvements:

• Training Phase: Trident achieves improvements up to 251.84x in iteration efficiency in local area network (LAN) settings over ABY3 and similarly reduces organizational costs by minimizing active participant requirements in wide area network (WAN) settings.
• Prediction Phase: The gains are equally compelling, with prediction phases showing throughput enhancement by factors ranging from 3x to over 600x.

Practical and Theoretical Implications

The implications of this research are twofold:

1. Practical Efficiency: Trident’s improvements indicate a marked step forward in secure machine learning applications at a substantial computational economy, offering a compelling framework for industries reliant on private data.
2. Theoretical Grounding: The research lays the groundwork for extending these paradigms to n-party computations, addressing scalability while maintaining security and performance. The paper also opens avenues for future work in guaranteeing output delivery under malicious settings without compromising throughput and efficiency.

Conclusion

The Trident framework ushers in a new synthesis of efficiency and security under the 4PC model, establishing itself as an indispensable tool for secure and practical machine learning deployments in sensitive data environments. This ability to boost operational speeds while reducing resource consumption represents a notable advancement in the domain, promising new capabilities and more robust protections for privacy-centric applications.