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arXiv · 2309.17124

Mostree : Malicious Secure Private Decision Tree Evaluation with Sublinear Communication

Jianli Bai·Xiangfu Song·Xiaowu Zhang·Qifan Wang·Shujie Cui·Ee-Chien Chang·Giovanni Russello
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Abstract

A private decision tree evaluation (PDTE) protocol allows a feature vector owner (FO) to classify its data using a tree model from a model owner (MO) and only reveals an inference result to the FO. This paper proposes Mostree, a PDTE protocol secure in the presence of malicious parties with sublinear communication. We design Mostree in the three-party honest-majority setting, where an (untrusted) computing party (CP) assists the FO and MO in the secure computation. We propose two low-communication oblivious selection (OS) protocols by exploiting nice properties of three-party replicated secret sharing (RSS) and distributed point function. Mostree combines OS protocols with a tree encoding method and three-party secure computation to achieve sublinear communication. We observe that most of the protocol components already maintain privacy even in the presence of a malicious adversary, and what remains to achieve is correctness. To ensure correctness, we propose a set of lightweight consistency checks and seamlessly integrate them into Mostree. As a result, Mostree achieves sublinear communication and malicious security simultaneously. We implement Mostree and compare it with the state-of-the-art. Experimental results demonstrate that Mostree is efficient and comparable to semi-honest PDTE schemes with sublinear communication. For instance, when evaluated on the MNIST dataset in a LAN setting, Mostree achieves an evaluation using approximately 768 ms with communication of around 168 KB.

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Jianli Bai, Xiangfu Song, Xiaowu Zhang, Qifan Wang, Shujie Cui, Ee-Chien Chang, Giovanni Russello. 2023-09-29. Mostree : Malicious Secure Private Decision Tree Evaluation with Sublinear Communication. https://arxiv.org/abs/2309.17124

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