Enhanced Privacy Preserving for Social Networks Relational Data Based on Personalized Differential Privacy

Kang, Haiyan; JI, Yuanrui; ZHANG, Shuxuan

doi:10.1049/cje.2021.00.274

Cited by 9 publications

(3 citation statements)

References 18 publications

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“…After using the FedAvg algorithm to handle data imbalance, the data is balanced, and privacy protection research can be conducted using the data of each participating user. There is a risk of data theft during the upload process [20][21][22] . To reduce data risk, enhance attack resistance and privacy protection, the study combines CNN and differential privacy to construct a DPAGD-CNN model with adaptive gradient descent.…”

Section: Construction Of Dpagd-cnn Privacy Protection Model On the Gr...mentioning

confidence: 99%

“…To further improve the protection level of personal data privacy, differential privacy has become a commonly used privacy protection technology. Differential privacy protects sensitive personal information by adding noise to the original data to alter the dataset [5][6] . In privacy data, data imbalance refers to a situation where the sample size varies greatly between different categories in a dataset.…”

Section: Introductionmentioning

confidence: 99%

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Privacy Protection Based on Federated Learning

Liu,

Eric B. Blancaflor,

Fang

et al. 2024

JAIT

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With the development of artificial intelligence technology, more and more fields will collect relevant user data, and provide users with a better experience through data analysis. But there are also risks involved in the process of data collection, namely how to protect personal privacy data. To address this issue, this study combined differential privacy, convolutional neural networks, and federated averaging algorithms to construct a privacy protection model. The study first utilized the federated average algorithm to handle data imbalance, ensuring that each analyzed data is in a balanced state. Then, based on of data balancing, a new algorithm model was constructed using differential privacy and convolutional neural networks. Finally, it utilized a number of public datasets to verify the role of the model in privacy protection. The results showed that the model can achieve recognition accuracy of 97.27% and 93.15%, respectively , for data under the influence of privacy budget and relaxation factor. Meanwhile, the classification accuracy of the model for data can reached 95.31%, with a regression error of 9.03%. When the local iteration number of the device was 30, the testing accuracy can reached 95.28%. This indicates that methods on the grounds of federated averaging algorithm and differential privacy can maintain the accuracy of the model while protecting user privacy. The application research of models has strong practical significance.

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Section: Construction Of Dpagd-cnn Privacy Protection Model On the Gr...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Privacy Protection Based on Federated Learning

Liu,

Eric B. Blancaflor,

Fang

et al. 2024

JAIT

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“…Multi-scale feature extraction refers to considering feature information of different scales simultaneously in the network [50]. Low-level features are mainly concentrated in the first two layers of convolution.…”

Section: Multi-scale Feature Extractionmentioning

confidence: 99%

An Enhanced Detection Method of PCB Defect Based on D-DenseNet (PCBDD-DDNet)

Kang,

Yang

2023

Electronics

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Printed Circuit Boards (PCBs), as integral components of electronic products, play a crucial role in modern industrial production. However, due to the precision and complexity of PCBs, existing PCB defect detection methods exhibit some issues such as low detection accuracy and limited usability. In order to address these problems, a PCB defect detection method based on D-DenseNet (PCBDD-DDNet) has been proposed. This method capitalizes on the advantages of two deep learning networks, CDBN (Convolutional Deep Belief Networks) and DenseNet (Densely Connected Convolutional Networks), to construct the D-DenseNet (Combination of CDBN and DenseNet) network. Within this network, CDBN focuses on extracting low-level features, while DenseNet is responsible for high-level feature extraction. The outputs from both networks are integrated using a weighted averaging approach. Additionally, the D-DenseNet employs a multi-scale module to extract features from different levels. This is achieved by incorporating filters of sizes 3 × 3, 5 × 5, and 7 × 7 along the three paths of the CDBN network, multi-scale feature extraction network, and DenseNet network, effectively capturing information at various scales. To prevent overfitting and enhance network performance, the Adafactor optimization function and L2 regularization are introduced. Finally, online hard example mining mechanism (OHEM) is incorporated to improve the network’s handling of challenging samples and enhance the accuracy of the PCB defect detection network. The effectiveness of this PCBDD-DDNet method is demonstrated through experiments conducted on publicly available PCB datasets. And the method achieves a mAP (mean Average Precision) of 93.24%, with an accuracy higher than other classical networks. The results affirm the method’s efficacy in PCB defect detection.

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Research on Federated Sharing Methods for Massive Data in Blockchain

Wu,

Kang

2024

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Enhanced Privacy Preserving for Social Networks Relational Data Based on Personalized Differential Privacy

Cited by 9 publications

References 18 publications

Privacy Protection Based on Federated Learning

Privacy Protection Based on Federated Learning

An Enhanced Detection Method of PCB Defect Based on D-DenseNet (PCBDD-DDNet)

Research on Federated Sharing Methods for Massive Data in Blockchain

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