PriVDT: An Efficient Two-Party Cryptographic Framework for Vertical Decision Trees

Chen, Hanxiao; Wang, Yingzhe; Xu, Guowen; Zhang, Tianwei

doi:10.1109/tifs.2022.3231784

Cited by 10 publications

(2 citation statements)

References 55 publications

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“…Like most privacy-preserving techniques based on FSS [28,[40][41][42], their schemes derive correlated randomness through a third party. However, the role of the third party can be jointly simulated by the two parties using either generic two-party secure protocols such as garbled circuits (GCs) [43] and GMW [44], or specifc techniques [45].…”

Section: Related Workmentioning

confidence: 99%

Secure Two-Party Decision Tree Classification Based on Function Secret Sharing

Liu,

Tang

2023

Complexity

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Decision tree models are widely used for classification tasks in data mining. However, privacy becomes a significant concern when training data contain sensitive information from different parties. This paper proposes a novel framework for secure two-party decision tree classification that enables collaborative training and evaluation without leaking sensitive data. The critical techniques employed include homomorphic encryption, function secret sharing (FSS), and a custom secure comparison protocol. Homomorphic encryption allows computations on ciphertexts, enabling parties to evaluate an encrypted decision tree model jointly. FSS splits functions into secret shares to hide sensitive intermediate values. The comparison protocol leverages FSS to securely compare attribute values to node thresholds for tree traversal, reducing overhead through efficient cryptographic techniques. Our framework divides computation between two servers holding private data. A privacy-preserving protocol lets them jointly construct a decision tree classifier without revealing their respective inputs. The servers encrypt their data and exchange function secret shares to traverse the tree and obtain the classification result. Rigorous security proofs demonstrate that the protocol protects data confidentiality in a semihonest model. Experiments on benchmark datasets confirm that the approach achieves high accuracy with reasonable computation and communication costs. The techniques minimize accuracy loss and latency compared to prior protocols. Overall, the paper delivers an efficient, modular framework for practical two-party secure decision tree evaluation that advances the capability of privacy-preserving machine learning.

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Section: Related Workmentioning

confidence: 99%

Secure Two-Party Decision Tree Classification Based on Function Secret Sharing

Liu,

Tang

2023

Complexity

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“…Device-to-device (D2D) networks are considered as an innovative feature of the 5G/6G communications [10]. Thus, many previous works also study using UAVs as aerial relays in the D2D networks [11] [12].…”

Section: Introductionmentioning

confidence: 99%

Resource Scheduling for UAVs-Aided D2D Networks: A Multi-Objective Optimization Approach

Pan,

Liu,

Sun

et al. 2024

IEEE Trans. Wireless Commun.

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Unmanned aerial vehicles (UAVs)-aided device-todevice (D2D) networks have attracted great interests with the development of 5G/6G communications, while there are several challenges about resource scheduling in UAVs-aided D2D networks. In this work, we formulate a UAVs-aided D2D network resource scheduling optimization problem (NetResSOP) to comprehensively consider the number of deployed UAVs, UAV positions, UAV transmission powers, UAV flight velocities, communication channels, and UAV-device pair assignment so as to maximize the D2D network capacity, minimize the number of deployed UAVs, and minimize the average energy consumption over all UAVs simultaneously. The formulated NetResSOP is a mixed-integer programming problem (MIPP) and an NPhard problem, which means that it is difficult to be solved in polynomial time. Moreover, there are trade-offs between the optimization objectives, and hence it is also difficult to find an optimal solution that can simultaneously make all objectives be optimal. Thus, we propose a non-dominated sorting genetic algorithm-III with a Flexible solution dimension mechanism, a Discrete part generation mechanism, and a UAV number adjustment mechanism (NSGA-III-FDU) for solving the problem comprehensively. Simulation results demonstrate the effectiveness and the stability of the proposed NSGA-III-FDU under different scales and settings of the D2D networks.

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