Towards Secure and Efficient Outsourcing of Machine Learning Classification

Zheng, Yifeng; Duan, Huayi

doi:10.1007/978-3-030-29959-0_2

Cited by 34 publications

(56 citation statements)

References 28 publications

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“…The scheme is tested on several widely used real-world datasets. The experimental results show that compared with the most recent work, i.e., Zheng et al's work [20], our scheme is more efficient when dealing with deeper trees. Particularly, the communication cost of our scheme is just 1/709 of Zheng et al's work [20].…”

mentioning

confidence: 69%

“…Discussion. There already exist several protocols about privacy-preserving comparison of two additive shared integers [20][21][22]32]. First, we should note that the setting of these comparisons is different from ours.…”

Section: Secure Comparison Algorithmmentioning

confidence: 90%

“…We newly design a secure comparison protocol that can return additive shares of the comparison result on additively secret shared inputs. Compared with the Huang et al's work [21] and Zheng et al's work [20], the number of additive multiplications required can be reduced from 2l and 3l to l respectively, where l is the bit-length of a feature vector's element. Compared with Liu et al's work [22], which is based on additive secret sharing and additively homomorphic cryptosystem, the proposed work is more secure and efficient.…”

Section: Our Contributionsmentioning

confidence: 95%

“…REMARK 3. In Zheng [20] et al's work, the two clouds blind the path cost along with the classification labels together and send them to the SU. Therefore, in their scheme, the SU needs to calculate more blinded path costs to recover the classification result.…”

Section: Secure Decision Tree Evaluationmentioning

confidence: 99%

“…Obviously, such a scheme cannot be adopted for large-scale trees. Zheng et al [20] proposed a work where the model is outsourced to the cloud from the model provider, e.g., a hospital, and the cloud with this outsourced model can provide decision tree evaluation service. Zheng et al's scheme is designed based on the two-cloud model.…”

Section: Introductionmentioning

confidence: 99%

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Towards an Efficient Privacy-Preserving Decision Tree Evaluation Service in the Internet of Things

Liu

Zhao

et al. 2020

Symmetry

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With the fast development of the Internet of Things (IoT) technology, normal people and organizations can produce massive data every day. Due to a lack of data mining expertise and computation resources, most of them choose to use data mining services. Unfortunately, directly sending query data to the cloud may violate their privacy. In this work, we mainly consider designing a scheme that enables the cloud to provide an efficient privacy-preserving decision tree evaluation service for resource-constrained clients in the IoT. To design such a scheme, a new secure comparison protocol based on additive secret sharing technology is proposed in a two-cloud model. Then we introduce our privacy-preserving decision tree evaluation scheme which is designed by the secret sharing technology and additively homomorphic cryptosystem. In this scheme, the cloud learns nothing of the query data and classification results, and the client has no idea of the tree. Moreover, this scheme also supports offline users. Theoretical analyses and experimental results show that our scheme is very efficient. Compared with the state-of-art work, both the communication and computational overheads of the newly designed scheme are smaller when dealing with deep but sparse trees.

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confidence: 69%