多方协作安全推理稀疏决策树，防侧信道且提速
Enhanced Outsourced and Secure Inference for Tall Sparse Decision Trees
- 将决策树按层拆分存于多个参与方，实现分布式安全推理
- 非完整树场景下平均运行时间显著优化，性能提升明显
- 抵御侧信道攻击，适合隐私敏感的云推理场景
决策树是广泛用于分类任务的可解释工具。一方面，为保护用户输入隐私，亟需构建能隐藏输入信息的隐私保护分类器；另一方面，随着云计算兴起，数据所有者希望将模型外包以降低风险，但需确保第三方无法窃取决策树模型。为此，Joye和Salehi提出一种理论协议，利用抗时序攻击的比较协议实现高效决策树评估，但该方法效率低下且易受侧信道攻击。本文提出一种新的决策树推理协议，将模型按层级拆分并由多个实体共同存储与计算，每个层级由一个称为“level-site”的新实体负责。该方法在非完整树场景下显著提升了平均评估速度，同时具备强侧信道攻击防护能力。
原文摘要 · Abstract (English)
A decision tree is an easy-to-understand tool that has been widely used for classification tasks. On the one hand, due to privacy concerns, there has been an urgent need to create privacy-preserving classifiers that conceal the user's input from the classifier. On the other hand, with the rise of cloud computing, data owners are keen to reduce risk by outsourcing their model, but want security guarantees that third parties cannot steal their decision tree model. To address these issues, Joye and Salehi introduced a theoretical protocol that efficiently evaluates decision trees while maintaining privacy by leveraging their comparison protocol that is resistant to timing attacks. However, their approach was not only inefficient but also prone to side-channel attacks. Therefore, in this paper, we propose a new decision tree inference protocol in which the model is shared and evaluated among multiple entities. We partition our decision tree model by each level to be stored in a new entity we refer to as a "level-site." Utilizing this approach, we were able to gain improved average run time for classifier evaluation for a non-complete tree, while also having strong mitigations against side-channel attacks.
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