Ents: An Efficient Three-party Training Framework for Decision Trees by Communication Optimization

Guopeng Lin, Weili Han, Wenqiang Ruan, Ruisheng Zhou, Lushan Song, Bingshuai Li, Yunfeng Shao·June 12, 2024

Summary

The paper introduces Ents, a three-party training framework for decision trees that enhances privacy and communication efficiency in secure multi-party computation. Ents employs secure radix sort and a share conversion protocol to reduce communication during dataset splitting and computation on large rings. Experimental results show significant improvements in communication sizes (5.5-9.3x), rounds (3.9-5.3x), and training time (3.5-6.7x) compared to existing methods. The framework is practical, enabling real-world dataset training within hours, even in WAN settings, and outperforms two-party frameworks, homomorphic encryption-based approaches, and non-open-sourced alternatives. The paper also presents protocols for secret-sharing operations, permutation management, and a security analysis. Overall, Ents demonstrates a novel and efficient solution for privacy-preserving decision tree training.

Key findings

13

Paper digest

What problem does the paper attempt to solve? Is this a new problem?

The paper "Ents: An Efficient Three-party Training Framework for Decision Trees by Communication Optimization" aims to address the issue of high communication overhead and inefficiency in existing training frameworks for decision trees . This problem is not new, as it has been identified in previous frameworks based on homomorphic encryption and share conversion protocols, which suffer from limitations in accuracy, efficiency, and impracticality . The paper proposes the Ents framework as a solution to reduce communication overhead and improve efficiency in training decision trees securely among three parties .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the scientific hypothesis related to the efficiency and performance of a three-party training framework for decision trees by optimizing communication . The study focuses on comparing the efficiency of the proposed framework, named Ents, with existing frameworks based on homomorphic encryption and share conversion protocols . The hypothesis revolves around demonstrating that the two-party Ents framework outperforms other frameworks in terms of communication efficiency, especially in the LAN and WAN settings, due to its reliance on additive secret sharing for computations .


What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?

The paper proposes a novel three-party training framework called Ents for decision trees, aiming to optimize communication overhead and ensure data privacy during the training process . The framework introduces two key optimizations to reduce communication overhead:

  1. Training Protocols based on Secure Radix Sort Protocols: Ents presents a series of training protocols leveraging secure radix sort protocols to efficiently and securely split datasets with continuous attributes. These protocols update pre-generated permutations to be compatible with group-wise protocols and securely split datasets, enabling Ents to train decision trees with linearly growing communication .
  2. Efficient Share Conversion Protocol: Ents introduces an efficient share conversion protocol to convert shares between a small ring and a large ring, reducing the significant communication overhead incurred by performing computations on a large ring. This protocol ensures correctness while improving efficiency in the training process .

Moreover, the paper highlights the security aspects of Ents, designed to operate under a three-party semi-honest security model with an honest majority. The security of the proposed conversion protocol is analyzed using the real/ideal world paradigm to ensure data privacy and integrity during training .

Additionally, Ents is evaluated for practical usage in privacy-preserving training for decision trees. Experimental results demonstrate that Ents outperforms existing frameworks in terms of communication sizes and rounds, showing significant improvements in efficiency and training time. Notably, Ents can train a decision tree on a real-world dataset with over 245,000 samples in less than three hours in the WAN setting, showcasing its promising practical application in privacy-preserving machine learning . The Ents three-party training framework for decision trees introduces key optimizations to reduce communication overhead and ensure data privacy during training, offering several advantages over previous methods .

Characteristics:

  1. Secure Radix Sort Protocols: Ents leverages secure radix sort protocols to efficiently and securely split datasets with continuous attributes, updating pre-generated permutations to align with group-wise protocols .
  2. Efficient Share Conversion Protocol: The framework introduces an efficient share conversion protocol to convert shares between small and large rings, reducing communication overhead significantly .

Advantages:

  1. Data Privacy: Ents ensures data privacy during the training process, maintaining the same accuracy level as plaintext training algorithms while outperforming existing frameworks in terms of efficiency .
  2. Efficiency: Ents demonstrates superior efficiency compared to previous methods, offering improvements in communication sizes, communication rounds, and training time. Experimental results show that Ents outperforms state-of-the-art frameworks by 5.5× ∼ 9.3× in communication sizes and 3.9× ∼ 5.3× in communication rounds, with a training time improvement of 3.5× ∼ 6.7× .

By optimizing communication overhead and ensuring data privacy, Ents presents a promising solution for privacy-preserving training for decision trees, showcasing significant advancements in efficiency and practical usability compared to existing methods .


Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?

Several related research studies have been conducted in the field of secure multi-party computation for decision tree training. Noteworthy researchers in this area include Akavia et al., Kelkar et al., Mohassel and Rindal, Aly et al., Gupta et al., Jawalkar et al., and many others . The key to the solution mentioned in the paper is the development of an efficient three-party training framework called Ents, which focuses on communication optimization to enhance the performance of decision tree training in a secure multi-party computation setting. Ents significantly outperforms existing frameworks by utilizing additive secret sharing for computations, which is more efficient than homomorphic encryption, leading to improved training times and reduced communication overhead .


How were the experiments in the paper designed?

The experiments in the paper "Ents: An Efficient Three-party Training Framework for Decision Trees by Communication Optimization" were designed to evaluate the performance of the proposed framework. The experiments were conducted on various datasets, including Kohkiloyeh, Diagnosis, Iris, Wine, Cancer, Tic-tac-toe, Adult, and Skin Segmentation, with different numbers of samples, attributes, and labels . The experiments were carried out on a Linux server with specific hardware specifications, such as a 32-core 2.4 GHz Intel Xeon CPU and 512GB of RAM. Each party in the framework was simulated by a separate process with four threads, and the network settings considered both LAN and WAN scenarios with specific bandwidth and round-trip time parameters . The performance evaluation results demonstrated the efficiency of the proposed Ents framework compared to existing two-party frameworks, showing significant improvements in training time in both LAN and WAN settings .


What is the dataset used for quantitative evaluation? Is the code open source?

The dataset used for quantitative evaluation in the Ents framework is not explicitly mentioned in the provided context. However, the implementation codes for Ents have been made open source and can be accessed at the GitHub repository: https://github.com/FudanMPL/Garnet/tree/Ents .


Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.

The experiments and results presented in the paper provide strong support for the scientific hypotheses that need to be verified. The paper outlines detailed experimental setups and results, comparing the performance of the proposed framework with existing frameworks . The experimental results demonstrate significant improvements in efficiency, outperforming other frameworks by a large margin in both LAN and WAN settings . This indicates that the proposed framework effectively addresses the challenges associated with communication overhead and computational efficiency in multi-party training frameworks for decision trees . The thorough experimental evaluation conducted in the paper enhances the credibility of the proposed framework and validates its effectiveness in achieving the intended scientific objectives .


What are the contributions of this paper?

The paper "Ents: An Efficient Three-party Training Framework for Decision Trees by Communication Optimization" makes several key contributions:

  • It introduces an efficient three-party training framework called Ents, which allows three parties to train a decision tree while preserving data privacy .
  • The paper proposes optimizations to reduce communication overhead in multi-party training frameworks for decision trees, addressing issues such as secure computations for splitting criteria and intermediate/final results representation .
  • Ents leverages secure radix sort protocols to efficiently and securely split datasets with continuous attributes, updating pre-generated permutations to be compatible with group-wise protocols, thereby reducing communication overhead .
  • The framework of Ents is designed to be secure under a three-party semi-honest security model with an honest majority, ensuring the privacy of the data during the training process .
  • Ents demonstrates promising practical usage in privacy-preserving training for decision trees, showing efficient training times on real-world datasets with over 245,000 samples .
  • The paper evaluates the accuracy of Ents by comparing it with the plaintext training algorithm for decision trees in scikit-learn, showing comparable accuracy results .
  • Ents outperforms existing two-party frameworks in terms of training time efficiency, showcasing significant improvements in both LAN and WAN settings .

What work can be continued in depth?

To delve deeper into the topic, further research can be conducted on the following aspects related to Ents, the three-party training framework for decision trees:

  1. Enhancing Secure Multi-party Computation (MPC): Explore advancements in MPC protocols, such as secret sharing-based protocols, homomorphic encryption-based protocols, and garbled circuit-based protocols . Investigate how these protocols can be further optimized for efficiency and security in multi-party scenarios.

  2. Privacy-Preserving Training Methods: Investigate the development of more efficient and secure methods for privacy-preserving training of decision trees, especially in scenarios where multiple parties need to collaborate while keeping their data private . This could involve exploring new encryption techniques or protocols to minimize communication overhead and ensure data privacy compliance.

  3. Scalability and Performance Optimization: Research ways to enhance the scalability and performance of Ents, particularly in handling large datasets and improving training times . This could involve optimizing communication protocols, memory usage, and computational efficiency to achieve faster and more effective decision tree training.

By focusing on these areas, researchers can further advance the field of privacy-preserving machine learning and decision tree training, contributing to more efficient and secure collaborative data analysis across multiple parties.

Tables

3

Introduction
Background
Evolution of secure computation in machine learning
Challenges in privacy and communication efficiency
Objective
To develop a novel framework for decision tree training
Enhance privacy and communication efficiency in secure MPC
Outperform existing methods
Method
Data Collection and Splitting
Secure Radix Sort
Implementation for efficient dataset partitioning
Share Conversion Protocol
Reducing communication during computation on large rings
Computation and Optimization
Decision Tree Construction
Privacy-preserving algorithms for tree growth
Communication Efficiency Techniques
Protocol design for secret-sharing operations
Permutation management strategies
Performance Evaluation
Experimental Results
Communication size reduction (5.5-9.3x)
Rounds reduction (3.9-5.3x)
Training time improvement (3.5-6.7x)
Real-world dataset training times and WAN settings
Comparison
Two-party frameworks
Homomorphic encryption-based approaches
Non-open-sourced alternatives
Security Analysis
Threat model and assumptions
Security guarantees (e.g., privacy leakage bounds)
Protocol robustness against attacks
Practicality and Applications
Real-world use cases
Scalability and deployment considerations
Case studies and examples
Conclusion
Summary of key contributions
Future research directions
Implications for privacy-preserving machine learning in distributed environments
Basic info
papers
cryptography and security
artificial intelligence
Advanced features
Insights
How does Ents enhance privacy and communication efficiency during secure multi-party computation?
What techniques does Ents use to reduce communication during dataset splitting and computation on large rings?
What is Ents and its primary purpose in the paper?
How do the experimental results compare Ents to existing methods in terms of improvements in communication, rounds, and training time?

Ents: An Efficient Three-party Training Framework for Decision Trees by Communication Optimization

Guopeng Lin, Weili Han, Wenqiang Ruan, Ruisheng Zhou, Lushan Song, Bingshuai Li, Yunfeng Shao·June 12, 2024

Summary

The paper introduces Ents, a three-party training framework for decision trees that enhances privacy and communication efficiency in secure multi-party computation. Ents employs secure radix sort and a share conversion protocol to reduce communication during dataset splitting and computation on large rings. Experimental results show significant improvements in communication sizes (5.5-9.3x), rounds (3.9-5.3x), and training time (3.5-6.7x) compared to existing methods. The framework is practical, enabling real-world dataset training within hours, even in WAN settings, and outperforms two-party frameworks, homomorphic encryption-based approaches, and non-open-sourced alternatives. The paper also presents protocols for secret-sharing operations, permutation management, and a security analysis. Overall, Ents demonstrates a novel and efficient solution for privacy-preserving decision tree training.
Mind map
Real-world dataset training times and WAN settings
Training time improvement (3.5-6.7x)
Rounds reduction (3.9-5.3x)
Communication size reduction (5.5-9.3x)
Permutation management strategies
Protocol design for secret-sharing operations
Privacy-preserving algorithms for tree growth
Reducing communication during computation on large rings
Implementation for efficient dataset partitioning
Non-open-sourced alternatives
Homomorphic encryption-based approaches
Two-party frameworks
Experimental Results
Communication Efficiency Techniques
Decision Tree Construction
Share Conversion Protocol
Secure Radix Sort
Outperform existing methods
Enhance privacy and communication efficiency in secure MPC
To develop a novel framework for decision tree training
Challenges in privacy and communication efficiency
Evolution of secure computation in machine learning
Implications for privacy-preserving machine learning in distributed environments
Future research directions
Summary of key contributions
Case studies and examples
Scalability and deployment considerations
Real-world use cases
Protocol robustness against attacks
Security guarantees (e.g., privacy leakage bounds)
Threat model and assumptions
Comparison
Performance Evaluation
Computation and Optimization
Data Collection and Splitting
Objective
Background
Conclusion
Practicality and Applications
Security Analysis
Method
Introduction
Outline
Introduction
Background
Evolution of secure computation in machine learning
Challenges in privacy and communication efficiency
Objective
To develop a novel framework for decision tree training
Enhance privacy and communication efficiency in secure MPC
Outperform existing methods
Method
Data Collection and Splitting
Secure Radix Sort
Implementation for efficient dataset partitioning
Share Conversion Protocol
Reducing communication during computation on large rings
Computation and Optimization
Decision Tree Construction
Privacy-preserving algorithms for tree growth
Communication Efficiency Techniques
Protocol design for secret-sharing operations
Permutation management strategies
Performance Evaluation
Experimental Results
Communication size reduction (5.5-9.3x)
Rounds reduction (3.9-5.3x)
Training time improvement (3.5-6.7x)
Real-world dataset training times and WAN settings
Comparison
Two-party frameworks
Homomorphic encryption-based approaches
Non-open-sourced alternatives
Security Analysis
Threat model and assumptions
Security guarantees (e.g., privacy leakage bounds)
Protocol robustness against attacks
Practicality and Applications
Real-world use cases
Scalability and deployment considerations
Case studies and examples
Conclusion
Summary of key contributions
Future research directions
Implications for privacy-preserving machine learning in distributed environments
Key findings
13

Paper digest

What problem does the paper attempt to solve? Is this a new problem?

The paper "Ents: An Efficient Three-party Training Framework for Decision Trees by Communication Optimization" aims to address the issue of high communication overhead and inefficiency in existing training frameworks for decision trees . This problem is not new, as it has been identified in previous frameworks based on homomorphic encryption and share conversion protocols, which suffer from limitations in accuracy, efficiency, and impracticality . The paper proposes the Ents framework as a solution to reduce communication overhead and improve efficiency in training decision trees securely among three parties .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the scientific hypothesis related to the efficiency and performance of a three-party training framework for decision trees by optimizing communication . The study focuses on comparing the efficiency of the proposed framework, named Ents, with existing frameworks based on homomorphic encryption and share conversion protocols . The hypothesis revolves around demonstrating that the two-party Ents framework outperforms other frameworks in terms of communication efficiency, especially in the LAN and WAN settings, due to its reliance on additive secret sharing for computations .


What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?

The paper proposes a novel three-party training framework called Ents for decision trees, aiming to optimize communication overhead and ensure data privacy during the training process . The framework introduces two key optimizations to reduce communication overhead:

  1. Training Protocols based on Secure Radix Sort Protocols: Ents presents a series of training protocols leveraging secure radix sort protocols to efficiently and securely split datasets with continuous attributes. These protocols update pre-generated permutations to be compatible with group-wise protocols and securely split datasets, enabling Ents to train decision trees with linearly growing communication .
  2. Efficient Share Conversion Protocol: Ents introduces an efficient share conversion protocol to convert shares between a small ring and a large ring, reducing the significant communication overhead incurred by performing computations on a large ring. This protocol ensures correctness while improving efficiency in the training process .

Moreover, the paper highlights the security aspects of Ents, designed to operate under a three-party semi-honest security model with an honest majority. The security of the proposed conversion protocol is analyzed using the real/ideal world paradigm to ensure data privacy and integrity during training .

Additionally, Ents is evaluated for practical usage in privacy-preserving training for decision trees. Experimental results demonstrate that Ents outperforms existing frameworks in terms of communication sizes and rounds, showing significant improvements in efficiency and training time. Notably, Ents can train a decision tree on a real-world dataset with over 245,000 samples in less than three hours in the WAN setting, showcasing its promising practical application in privacy-preserving machine learning . The Ents three-party training framework for decision trees introduces key optimizations to reduce communication overhead and ensure data privacy during training, offering several advantages over previous methods .

Characteristics:

  1. Secure Radix Sort Protocols: Ents leverages secure radix sort protocols to efficiently and securely split datasets with continuous attributes, updating pre-generated permutations to align with group-wise protocols .
  2. Efficient Share Conversion Protocol: The framework introduces an efficient share conversion protocol to convert shares between small and large rings, reducing communication overhead significantly .

Advantages:

  1. Data Privacy: Ents ensures data privacy during the training process, maintaining the same accuracy level as plaintext training algorithms while outperforming existing frameworks in terms of efficiency .
  2. Efficiency: Ents demonstrates superior efficiency compared to previous methods, offering improvements in communication sizes, communication rounds, and training time. Experimental results show that Ents outperforms state-of-the-art frameworks by 5.5× ∼ 9.3× in communication sizes and 3.9× ∼ 5.3× in communication rounds, with a training time improvement of 3.5× ∼ 6.7× .

By optimizing communication overhead and ensuring data privacy, Ents presents a promising solution for privacy-preserving training for decision trees, showcasing significant advancements in efficiency and practical usability compared to existing methods .


Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?

Several related research studies have been conducted in the field of secure multi-party computation for decision tree training. Noteworthy researchers in this area include Akavia et al., Kelkar et al., Mohassel and Rindal, Aly et al., Gupta et al., Jawalkar et al., and many others . The key to the solution mentioned in the paper is the development of an efficient three-party training framework called Ents, which focuses on communication optimization to enhance the performance of decision tree training in a secure multi-party computation setting. Ents significantly outperforms existing frameworks by utilizing additive secret sharing for computations, which is more efficient than homomorphic encryption, leading to improved training times and reduced communication overhead .


How were the experiments in the paper designed?

The experiments in the paper "Ents: An Efficient Three-party Training Framework for Decision Trees by Communication Optimization" were designed to evaluate the performance of the proposed framework. The experiments were conducted on various datasets, including Kohkiloyeh, Diagnosis, Iris, Wine, Cancer, Tic-tac-toe, Adult, and Skin Segmentation, with different numbers of samples, attributes, and labels . The experiments were carried out on a Linux server with specific hardware specifications, such as a 32-core 2.4 GHz Intel Xeon CPU and 512GB of RAM. Each party in the framework was simulated by a separate process with four threads, and the network settings considered both LAN and WAN scenarios with specific bandwidth and round-trip time parameters . The performance evaluation results demonstrated the efficiency of the proposed Ents framework compared to existing two-party frameworks, showing significant improvements in training time in both LAN and WAN settings .


What is the dataset used for quantitative evaluation? Is the code open source?

The dataset used for quantitative evaluation in the Ents framework is not explicitly mentioned in the provided context. However, the implementation codes for Ents have been made open source and can be accessed at the GitHub repository: https://github.com/FudanMPL/Garnet/tree/Ents .


Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.

The experiments and results presented in the paper provide strong support for the scientific hypotheses that need to be verified. The paper outlines detailed experimental setups and results, comparing the performance of the proposed framework with existing frameworks . The experimental results demonstrate significant improvements in efficiency, outperforming other frameworks by a large margin in both LAN and WAN settings . This indicates that the proposed framework effectively addresses the challenges associated with communication overhead and computational efficiency in multi-party training frameworks for decision trees . The thorough experimental evaluation conducted in the paper enhances the credibility of the proposed framework and validates its effectiveness in achieving the intended scientific objectives .


What are the contributions of this paper?

The paper "Ents: An Efficient Three-party Training Framework for Decision Trees by Communication Optimization" makes several key contributions:

  • It introduces an efficient three-party training framework called Ents, which allows three parties to train a decision tree while preserving data privacy .
  • The paper proposes optimizations to reduce communication overhead in multi-party training frameworks for decision trees, addressing issues such as secure computations for splitting criteria and intermediate/final results representation .
  • Ents leverages secure radix sort protocols to efficiently and securely split datasets with continuous attributes, updating pre-generated permutations to be compatible with group-wise protocols, thereby reducing communication overhead .
  • The framework of Ents is designed to be secure under a three-party semi-honest security model with an honest majority, ensuring the privacy of the data during the training process .
  • Ents demonstrates promising practical usage in privacy-preserving training for decision trees, showing efficient training times on real-world datasets with over 245,000 samples .
  • The paper evaluates the accuracy of Ents by comparing it with the plaintext training algorithm for decision trees in scikit-learn, showing comparable accuracy results .
  • Ents outperforms existing two-party frameworks in terms of training time efficiency, showcasing significant improvements in both LAN and WAN settings .

What work can be continued in depth?

To delve deeper into the topic, further research can be conducted on the following aspects related to Ents, the three-party training framework for decision trees:

  1. Enhancing Secure Multi-party Computation (MPC): Explore advancements in MPC protocols, such as secret sharing-based protocols, homomorphic encryption-based protocols, and garbled circuit-based protocols . Investigate how these protocols can be further optimized for efficiency and security in multi-party scenarios.

  2. Privacy-Preserving Training Methods: Investigate the development of more efficient and secure methods for privacy-preserving training of decision trees, especially in scenarios where multiple parties need to collaborate while keeping their data private . This could involve exploring new encryption techniques or protocols to minimize communication overhead and ensure data privacy compliance.

  3. Scalability and Performance Optimization: Research ways to enhance the scalability and performance of Ents, particularly in handling large datasets and improving training times . This could involve optimizing communication protocols, memory usage, and computational efficiency to achieve faster and more effective decision tree training.

By focusing on these areas, researchers can further advance the field of privacy-preserving machine learning and decision tree training, contributing to more efficient and secure collaborative data analysis across multiple parties.

Tables
3
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