preprint · arXiv (Cornell University) · 2023

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.

Cite this paper

APA 7

Bai, J., Song, X., Zhang, X., Wang, Q., Cui, S., Chang, E., & Russello, G. (2023). Mostree : Malicious Secure Private Decision Tree Evaluation with Sublinear Communication. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2309.17124

MLA 9

Bai, Jianli, et al. “Mostree : Malicious Secure Private Decision Tree Evaluation with Sublinear Communication.” arXiv (Cornell University), 2023, https://doi.org/10.48550/arxiv.2309.17124.

Chicago

Bai, Jianli, Xiangfu Song, Xiaowu Zhang, Qifan Wang, Shujie Cui, Ee‐Chien Chang, and Giovanni Russello. 2023. “Mostree : Malicious Secure Private Decision Tree Evaluation with Sublinear Communication.” arXiv (Cornell University). https://doi.org/10.48550/arxiv.2309.17124.

Harvard

Bai, J. et al. (2023) ‘Mostree : Malicious Secure Private Decision Tree Evaluation with Sublinear Communication’, arXiv (Cornell University). Available at: https://doi.org/10.48550/arxiv.2309.17124.

BibTeX

@misc{bai2023mostree,
  title = {{Mostree : Malicious Secure Private Decision Tree Evaluation with Sublinear Communication}},
  author = {Bai, Jianli and Song, Xiangfu and Zhang, Xiaowu and Wang, Qifan and Cui, Shujie and Chang, Ee‐Chien and Russello, Giovanni},
  journal = {arXiv (Cornell University)},
  year = {2023},
  doi = {10.48550/arxiv.2309.17124}
}

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