OnePath: Efficient and Privacy-Preserving Decision Tree Inference in the Cloud
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866908456355627008 |
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| author | Yuan, Shuai Li, Hongwei Qian, Xinyuan Xu, Guowen |
| author_facet | Yuan, Shuai Li, Hongwei Qian, Xinyuan Xu, Guowen |
| contents | The vast storage capacity and computational power of cloud servers have led to the widespread outsourcing of machine learning inference services. While offering significant operational benefits, this practice also introduces privacy risks, such as the exposure of proprietary models and sensitive user data. In this paper, we present OnePath, a framework for secure and efficient decision tree inference in cloud environments. Unlike existing methods that traverse all internal nodes of a decision tree, our traversal protocol processes only the nodes on the prediction path, significantly improving inference efficiency while preserving privacy. To further optimize privacy and performance, OnePath is the first to employ functional encryption for evaluating decision tree nodes. Notably, our protocol enables both model providers and users to remain offline during the inference phase, offering a crucial advantage for practical deployment. We provide formal security analysis to demonstrate that OnePath provides comprehensive privacy protections during the model inference process. Extensive experimental results show that our approach processes query data in microseconds, highlighting its efficiency. OnePath offers a practical solution that strikes a balance between security and performance, making it a promising option for a wide range of cloud-based decision tree inference applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_19334 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | OnePath: Efficient and Privacy-Preserving Decision Tree Inference in the Cloud Yuan, Shuai Li, Hongwei Qian, Xinyuan Xu, Guowen Cryptography and Security The vast storage capacity and computational power of cloud servers have led to the widespread outsourcing of machine learning inference services. While offering significant operational benefits, this practice also introduces privacy risks, such as the exposure of proprietary models and sensitive user data. In this paper, we present OnePath, a framework for secure and efficient decision tree inference in cloud environments. Unlike existing methods that traverse all internal nodes of a decision tree, our traversal protocol processes only the nodes on the prediction path, significantly improving inference efficiency while preserving privacy. To further optimize privacy and performance, OnePath is the first to employ functional encryption for evaluating decision tree nodes. Notably, our protocol enables both model providers and users to remain offline during the inference phase, offering a crucial advantage for practical deployment. We provide formal security analysis to demonstrate that OnePath provides comprehensive privacy protections during the model inference process. Extensive experimental results show that our approach processes query data in microseconds, highlighting its efficiency. OnePath offers a practical solution that strikes a balance between security and performance, making it a promising option for a wide range of cloud-based decision tree inference applications. |
| title | OnePath: Efficient and Privacy-Preserving Decision Tree Inference in the Cloud |
| topic | Cryptography and Security |
| url | https://arxiv.org/abs/2409.19334 |