Securely Outsourcing ID3 Decision Tree in Cloud Computing

Li, Ye; Jiang, Zoe L.; Wang, Xuan; Fang, Junbin; Zhang, En; Wang, Xianmin

doi:10.1155/2018/2385150

Cited by 6 publications

(4 citation statements)

References 38 publications

(39 reference statements)

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“…They conducted research on the security and privacy issues faced after the data was outsourced to the cloud server and conducted research and experimental analysis on the SVM and gradient descent algorithm, respectively. Li et al [34] solved the out-sourcing security challenge under the malicious model and used homomorphic encryption and security obfuscation circuit technology to implement the secure outsourcing calculation of the ID3 decision tree algorithm. Under the semihonest model, Zhang et al [35] designed a privacyprotected single-layer perceptron training scheme.…”

Section: Related Workmentioning

confidence: 99%

A Secure and Verifiable Outsourcing Scheme for Assisting Mobile Device Training Machine Learning Model

Yang

2020

Wireless Communications and Mobile Computing

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In smart applications such as smart medical equipment, more data needs to be processed and trained locally and near the local end to prevent privacy leaks. However, the storage and computing capabilities of smart devices are limited, so some computing tasks need to be outsourced; concurrently, the prevention of malicious nodes from accessing user data during outsourcing computing is required. Therefore, this paper proposes EVPP (efficient, verifiable, and privacy-preserving), which is a computing outsourcing scheme used in the training process of machine learning models. The edge nodes outsource the complex computing process to the edge service node. First, we conducted a certain amount of testing to confirm the parts that need to be outsourced. In this solution, the computationally intensive part of the model training process is outsourced. Meanwhile, a random encryption perturbation is performed on the outsourced training matrix, and verification factors are introduced to ensure the verifiability of the results. In addition, the system can generate verifiable evidence that can be generated to build a trust mechanism when a malicious service node is found. At the same time, this paper also discusses the application of the scheme in other algorithms in order to be better applied. Through the analysis of theoretical and experimental data, it can be shown that the scheme proposed in this paper can effectively use the computing power of the equipment.

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Section: Related Workmentioning

confidence: 99%

A Secure and Verifiable Outsourcing Scheme for Assisting Mobile Device Training Machine Learning Model

Yang

2020

Wireless Communications and Mobile Computing

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“…Ye Li et al 13 consider the multiparticipant's ciphertext problems in cloud computing. In IoT networks, huge data are to be processed which requires outsourced data storage facility such as the cloud.…”

Section: Related Workmentioning

confidence: 99%

A novel security mechanism using AES cryptography approach in cloud computing

Muthulakshmi

Venkatesulu

2020

Int J Communication

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Summary For a smart cloud computing system, security and privacy are the required factors. In recent days, the evolvement of cloud technology is immense, according to which several cloud service providers (CSPs) evolved. Each CSP has its infrastructure with its security policies. Even though still the security in cloud computing is an existing challenge, it is more important to maintain the trustiness of cloud environment among the cloud users. On this motto, we propose a novel tailored—advanced encryption standard (AES) Cryptographic Algorithm (TCA). AES is one of the popular cryptography algorithms. We indulged in increasing the performance of this algorithm by minimizing the time taken for the cryptography process. Here, the cryptography is processed with AES algorithm. In order to improve the efficiency of AES, a concept of a chunk file system is introduced. The input file is chunked into multiple which facilitates quick and effective encryption. To prove the efficiency of our proposed system, a comparison work is carried out with the existing algorithms lightweight keyword searchable encryption (LFSE) and cloud key management system (CKMS). The comparison work is based on the aspect of time taken during key generation, encryption and decryption. The obtained result is plotted, and comparison output clearly states our TCA's excellence in the aspect of efficiency compared to others.

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“…Although there have been numerous studies on the privacy-preserving ID3 Algorithm, these studies are limited to two-party horizontal partitioning data mode [7], or horizontal partitioning data model with more than two-party [8,9,10,11,12,13,14,15], or vertical partitioning data model with more than two-party [16,17,18,14]. Therefore, they cannot be applied to the 2PFD setting.…”

Section: Introductionmentioning

confidence: 99%

Privacy-Preserving Decision Tree Solution in the 2-Part Fully Distributed Setting

Van¹,

Dung²,

Quan³

et al. 2022

ISJ

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Abstract— Data mining has emerged as an important technology for obtaining knowledge from big data. However, there are growing concerns that the use of this technology is infringing on privacy. This work proposes a decision tree mining solution according to the ID3 algorithm that ensures privacy in the 2-Part Fully Distributed setting.Tóm tắt— Khai phá dữ liệu đã nổi lên như một công nghệ quan trọng để thu thập kiến thức từ lượng dữ liệu khổng lồ. Tuy nhiên, ngày càng có nhiều lo ngại rằng việc sử dụng công nghệ này đang vi phạm quyền riêng tư của cá nhân. Bài báo này đề xuất giải pháp khai phá cây quyết định theo thuật toán ID3 có đảm bảo tính riêng tư trong mô hình phân tán đầy đủ hai bên.

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Securely Outsourcing ID3 Decision Tree in Cloud Computing

Cited by 6 publications

References 38 publications

A Secure and Verifiable Outsourcing Scheme for Assisting Mobile Device Training Machine Learning Model

A Secure and Verifiable Outsourcing Scheme for Assisting Mobile Device Training Machine Learning Model

A novel security mechanism using AES cryptography approach in cloud computing

Privacy-Preserving Decision Tree Solution in the 2-Part Fully Distributed Setting

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