7 results on '"Xu, Chugui"'
Search Results
2. An RBF Neural Network Clustering Algorithm Based on K-Nearest Neighbor.
- Author
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Li, Jitao, Xu, Chugui, Liang, Yongquan, Wu, Gengkun, and Liang, Zhao
- Subjects
- *
K-nearest neighbor classification , *RADIAL basis functions , *CLASSIFICATION algorithms , *ALGORITHMS , *K-means clustering - Abstract
Neural network is a supervised classification algorithm which can deal with high complexity and nonlinear data analysis. Supervised algorithm needs some known labels in the training process, and then corrects parameters through backpropagation method. However, due to the lack of marked labels, existing literature mostly uses Auto-Encoder to reduce the dimension of data when facing of clustering problems. This paper proposes an RBF (Radial Basis Function) neural network clustering algorithm based on K-nearest neighbors theory, which first uses K-means algorithm for preclassification, and then constructs self-supervised labels based on K-nearest neighbors theory for backpropagation. The algorithm in this paper belongs to a self-supervised neural network clustering algorithm, and it also makes the neural network truly have the ability of self-decision-making and self-optimization. From the experimental results of the artificial data sets and the UCI data sets, it can be proved that the proposed algorithm has excellent adaptability and robustness. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
3. GANobfuscator: Mitigating Information Leakage Under GAN via Differential Privacy.
- Author
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Xu, Chugui, Ren, Ju, Zhang, Deyu, Zhang, Yaoxue, Qin, Zhan, and Ren, Kui
- Abstract
By learning generative models of semantic-rich data distributions from samples, generative adversarial network (GAN) has recently attracted intensive research interests due to its excellent empirical performance as a generative model. The model is used to estimate the underlying distribution of a dataset and randomly generate realistic samples according to their estimated distribution. However, GANs can easily remember training samples due to the high model complexity of deep networks. When GANs are applied to private or sensitive data, the concentration of distribution may divulge some critical information. It consequently requires new technological advances to mitigate the information leakage under GANs. To address this issue, we propose GANobfuscator, a differentially private GAN, which can achieve differential privacy under GANs by adding carefully designed noise to gradients during the learning procedure. With GANobfuscator, analysts are able to generate an unlimited amount of synthetic data for arbitrary analysis tasks without disclosing the privacy of training data. Moreover, we theoretically prove that GANobfuscator can provide strict privacy guarantee with differential privacy. In addition, we develop a gradient-pruning strategy for GANobfuscator to improve the scalability and stability of data training. Through extensive experimental evaluation on benchmark datasets, we demonstrate that GANobfuscator can produce high-quality generated data and retain desirable utility under practical privacy budgets. [ABSTRACT FROM AUTHOR]
- Published
- 2019
- Full Text
- View/download PDF
4. Distilling at the Edge: A Local Differential Privacy Obfuscation Framework for IoT Data Analytics.
- Author
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Xu, Chugui, Ren, Ju, Zhang, Deyu, and Zhang, Yaoxue
- Subjects
- *
INTERNET of things , *DATA analytics , *CLOUD computing , *DATA security , *WIRELESS communications - Abstract
Edge computing has emerged as a promising paradigm for delay-sensitive and context-aware IoT data analytics, through migrating data processing from the cloud to the edge of the network. However, traditional solutions adopting homomorphic encryption to achieve data protection and aggregation at edge servers are infeasible because of their heavy computational overhead. How to preserve data privacy while guaranteeing data utility in edge computing becomes an extremely important problem for IoT data analytics. In this article, we propose a local differential privacy obfuscation (LDPO) framework for IoT data analytics to aggregate and distill the IoT data at the edge without disclosing users' sensitive data. We first introduce the architecture and benefits of the LDPO framework, followed by some technical challenges in guaranteeing its performance. Then we present a preliminary implementation of the LDPO framework, and validate its performance in terms of privacy preservation level and data utility using real-world apps and datasets. Some future directions are finally envisioned for further research. [ABSTRACT FROM AUTHOR]
- Published
- 2018
- Full Text
- View/download PDF
5. DPPro: Differentially Private High-Dimensional Data Release via Random Projection.
- Author
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Xu, Chugui, Ren, Ju, Zhang, Yaoxue, Qin, Zhan, and Ren, Kui
- Abstract
Releasing representative data sets without compromising the data privacy has attracted increasing attention from the database community in recent years. Differential privacy is an influential privacy framework for data mining and data release without revealing sensitive information. However, existing solutions using differential privacy cannot effectively handle the release of high-dimensional data due to the increasing perturbation errors and computation complexity. To address the deficiency of existing solutions, we propose DPPro, a differentially private algorithm for high-dimensional data release via random projection to maximize utility while guaranteeing privacy. We theoretically prove that DPPro can generate synthetic data set with the similar squared Euclidean distance between high-dimensional vectors while achieving $(\epsilon,\delta)$ -differential privacy. Based on the theoretical analysis, we observed that the utility guarantees of released data depend on the projection dimension and the variance of the noise. Extensive experimental results demonstrate that DPPro substantially outperforms several state-of-the-art solutions in terms of perturbation error and privacy budget on high-dimensional data sets. [ABSTRACT FROM PUBLISHER]
- Published
- 2017
- Full Text
- View/download PDF
6. Serving at the Edge: A Scalable IoT Architecture Based on Transparent Computing.
- Author
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Ren, Ju, Guo, Hui, Xu, Chugui, and Zhang, Yaoxue
- Subjects
INTERNET of things ,COMPUTER architecture ,INFORMATION processing ,COMPUTING platforms ,WEARABLE technology - Abstract
By moving service provisioning from the cloud to the edge, edge computing becomes a promising solution in the era of IoT to meet the delay requirements of IoT applications, enhance the scalability and energy efficiency of lightweight IoT devices, provide contextual information processing, and mitigate the traffic burdens of the backbone network. However, as an emerging field of study, edge computing is still in its infancy and faces many challenges in its implementation and standardization. In this article, we study an implementation of edge computing, which exploits transparent computing to build scalable IoT platforms. Specifically, we first propose a transparent computing based IoT architecture, and clearly identify its advantages and associated challenges. Then, we present a case study to clearly show how to build scalable lightweight wearables with the proposed architecture. Some future directions are finally pointed out to foster continued research efforts. [ABSTRACT FROM AUTHOR]
- Published
- 2017
- Full Text
- View/download PDF
7. iProps: A comprehensive software tool for protein classification and analysis with automatic machine learning capabilities and model interpretation capabilities.
- Author
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Feng C, Wei H, Xu C, Feng B, Zhu X, Liu J, and Zou Q
- Abstract
Protein classification is a crucial field in bioinformatics. The development of a comprehensive tool that can perform feature evaluation, visualization, automated machine learning, and model interpretation would significantly advance research in protein classification. However, there is a significant gap in the literature regarding tools that integrate all these essential functionalities. This paper presents iProps, a novel Python-based software package, meticulously crafted to fulfill these multifaceted requirements. iProps is distinguished by its proficiency in feature extraction, evaluation, automated machine learning, and interpretation of classification models. Firstly, iProps fully leverages evolutionary information and amino acid reduction information to propose or extend several numerical protein features that are independent of sequence length, including SC-PSSM, ORDip, TRC, CTDC-E, CKSAAGP-E, and so forth; at the same time, it also implements the calculation of 17 other numerical features within the software. iProps also provides feature combination operations for the aforementioned features to generate more hybrid features, and has added data balancing sampling processing as well as built-in classifier settings, among other functionalities. Thus, It can discern the most effective protein class recognition feature from a multitude of candidates, utilizing three automated machine learning algorithms to identify the most optimal classifiers and parameter settings. Furthermore, iProps generates a detailed explanatory report that includes 23 informative graphs derived from three interpretable models. To assess the performance of iProps, a series of numerical experiments were conducted using two well-established datasets. The results demonstrated that our software achieved superior recognition performance in every case. Beyond its contributions to bioinformatics, iProps broadens its applicability by offering robust data analysis tools that are beneficial across various disciplines, capitalizing on its automated machine learning and model interpretation capabilities. As an open-source platform, iProps is readily accessible and features an intuitive user interface, ensuring ease of use for individuals, even those without a background in programming. The source code of the software is available for download at the following website: https://github.com/LigosQ/iProps and https://gitee.com/LigosQ/iProps.
- Published
- 2024
- Full Text
- View/download PDF
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