SecureBoost: A Lossless Federated Learning Framework (1901.08755v3)

Abstract: The protection of user privacy is an important concern in machine learning, as evidenced by the rolling out of the General Data Protection Regulation (GDPR) in the European Union (EU) in May 2018. The GDPR is designed to give users more control over their personal data, which motivates us to explore machine learning frameworks for data sharing that do not violate user privacy. To meet this goal, in this paper, we propose a novel lossless privacy-preserving tree-boosting system known as SecureBoost in the setting of federated learning. SecureBoost first conducts entity alignment under a privacy-preserving protocol and then constructs boosting trees across multiple parties with a carefully designed encryption strategy. This federated learning system allows the learning process to be jointly conducted over multiple parties with common user samples but different feature sets, which corresponds to a vertically partitioned data set. An advantage of SecureBoost is that it provides the same level of accuracy as the non-privacy-preserving approach while at the same time, reveals no information of each private data provider. We show that the SecureBoost framework is as accurate as other non-federated gradient tree-boosting algorithms that require centralized data and thus it is highly scalable and practical for industrial applications such as credit risk analysis. To this end, we discuss information leakage during the protocol execution and propose ways to provably reduce it.

• The paper introduces a privacy-preserving protocol that enables secure, collaborative model training without compromising the integrity of vertically partitioned data.
• It leverages the Paillier homomorphic encryption scheme to securely compute gradients and Hessians, maintaining lossless performance in distributed scenarios.
• The framework is scalable, demonstrating effective application in real-world settings such as credit risk analysis while meeting strict data protection standards.

SecureBoost: A Lossless Federated Learning Framework

The paper "SecureBoost: A Lossless Federated Learning Framework" addresses a crucial challenge in modern machine learning: achieving high-quality collaborative model training without compromising user privacy, especially when data is vertically partitioned among multiple parties. This framework is particularly relevant in scenarios where organizations must comply with stringent data protection regulations like GDPR, while still needing to build predictive models from combined datasets held by different entities.

Overview

SecureBoost is a federated learning system designed for privacy-preserving tree boosting. The proposed methodology allows multiple parties to collaboratively train a model without requiring data centralization, which aligns with data confidentiality requirements. The paper introduces an encryption protocol that ensures no party gains access to another's private data while achieving the same level of model accuracy as traditional non-privacy-preserving approaches.

Key Contributions

The SecureBoost framework is notable for several reasons:

1. Privacy-Preserving Protocol: The introduction of a privacy-preserving protocol for entity alignment ensures that only common users across datasets are identified, preserving the confidentiality of non-overlapping parts.
2. Effective Use of Homomorphic Encryption: By leveraging the Paillier encryption scheme, SecureBoost securely computes necessary statistics (such as gradients and Hessians) without exposing private data. This step is crucial in maintaining the confidentiality of the labels, which are only available to the active party.
3. Lossless Performance: The framework is demonstrated to be lossless, meaning the models trained under secure federated conditions achieve accuracy equivalent to models trained with centralized data. This property makes it both practical and scalable for real-world applications, as illustrated by the example of credit risk analysis.
4. Scalability: Preliminary experiments show that SecureBoost scales well with increasing data size and tree depth, maintaining efficiency without diminishing performance.

Discussion and Implications

The paper brings forth critical insights into the potential of federated learning in maintaining data privacy without sacrificing model performance. SecureBoost provides a pathway for different organizations, such as banks and retailers, to collaborate in data-rich environments without legal and ethical concerns over data sharing.

Additionally, the exploration of Reduced-Leakage SecureBoost (RL-SecureBoost) offers an improvement to reduce information leakage from the first tree by solely relying on features that are known to be confidentiality-safe. This adjustment is particularly relevant when balancing security concerns with the need for effective model training.

Future Directions

The research opens multiple avenues for future exploration:

• Generalizing to Other Machine Learning Models: Expanding the framework to include other types of models, such as neural networks or linear models, could widen its applicability in various domains.
• Further Security Enhancements: While SecureBoost already reduces information leakage significantly, integrating more advanced cryptographic protocols could enhance security further, particularly in scenarios with heightened privacy requirements.
• Scalability Enhancements: Optimizing SecureBoost for even larger datasets or more parties to evaluate its performance in massive-scale federated environments would provide deeper insights into its industrial applicability.

In conclusion, SecureBoost represents a significant advancement in federated learning frameworks, offering a robust solution that addresses both privacy and performance concerns in collaborative data-driven environments.