Securely and Efficiently Outsourcing Decision Tree Inference

Outsourcing machine learning inference services to the cloud is getting increasingly popular. However, this also entails privacy risks to the provider's proprietary model and the client's sensitive data. Focusing on inference with decision trees, this article proposes a framework for secur...

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Bibliographic Details
Published inIEEE transactions on dependable and secure computing Vol. 19; no. 3; pp. 1841 - 1855
Main Authors Zheng, Yifeng, Duan, Huayi, Wang, Cong, Wang, Ruochen, Nepal, Surya
Format Journal Article
LanguageEnglish
Published Washington IEEE 01.05.2022
IEEE Computer Society
Subjects
Cloud computing
Computational modeling
Cryptography
Decision trees
Inference
Machine learning
Machine learning inference
Outsourcing
Performance evaluation
Privacy
Protocols
secure outsourcing
Security
Servers
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Summary:Outsourcing machine learning inference services to the cloud is getting increasingly popular. However, this also entails privacy risks to the provider's proprietary model and the client's sensitive data. Focusing on inference with decision trees, this article proposes a framework for securely and efficiently outsourcing decision tree inference. Targeting both privacy and efficiency, we propose a customized protocol using only lightweight cryptography in the online execution of secure inference. We resort to additive secret sharing and tackle the problems in various components including secure input feature selection, decision node evaluation, and inference result generation. Our protocol requires no interaction from the provider and client during online secure inference, a distinct advantage over prior works for practical deployment as they all operate under the client-provider setting where synchronous and continuous interaction is required. Performance evaluation demonstrates our security design's efficiency, as well as substantial performance benefits for the client (up to four orders of magnitude in computation and 163 times in communication), as opposed to prior art in the non-outsourcing setting. To facilitate the practical usage for meeting more service demands, we also investigate the extensions for secure outsourced inference of random forests and categorical feature-based decision trees.
ISSN:1545-5971
1941-0018
DOI:10.1109/TDSC.2020.3040012