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Installation of the Automatic Noise Monitoring Network for Urban Functional Zones and Data Application 认领 引用
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作者 DUAN Lili 《外文科技期刊数据库（文摘版）自然科学》 2026年第1期066-070,共5页
Noise pollution is one of the major environmental pollution issues that harm people's quality of life. According to the "China Noise Pollution Prevention and Control Report (2024)" released by the Minist... Noise pollution is one of the major environmental pollution issues that harm people's quality of life. According to the "China Noise Pollution Prevention and Control Report (2024)" released by the Ministry of Ecology and Environment, in 2023, the number of public complaints about noise disturbances received by departments such as ecology and environment and public security in cities at or above the prefecture level nationwide (excluding municipalities directly under the central government, sub-provincial cities, and cities with independent planning status) reached approximately 5.7million cases, representing an increase of over 7%. To better address the increasingly severe noise pollution problem, the Ministry of Ecology and Environment issued the "Ten Measures for Sound Management" —the "14th Five-Year Plan for Noise Pollution Prevention and Control Action" —in 2023, explicitly proposing the comprehensive establishment of a regional automatic monitoring system for sound environmental quality: by January 1, 2025, all cities where provincial, autonomous region, or municipal governments are located, as well as cities with independent planning status, shall have established regional automatic monitoring stations for sound environmental quality connected to national and provincial platforms. After 2026, all cities at or above the prefecture level nationwide should participate in this automatic monitoring system. 展开更多
关键词 Functional area acoustic environment Automatic noise monitoring Network configuration Data utilization Disturbance level Voiceprint recognition
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Transforming waste to value:Enhancing battery lifetime prediction using incomplete data samples 认领 引用
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作者 Xiaoang Zhai Guohua Liu +4 位作者 Ting Lu Sihui Chen Yang Liu Jiayu Wan Xin Li 《Journal of Energy Chemistry》 SCIE EI CAS CSCD 2025年第7期642-649,共8页
The widespread usage of rechargeable batteries in portable devices,electric vehicles,and energy storage systems has underscored the importance for accurately predicting their lifetimes.However,data scarcity often limi... The widespread usage of rechargeable batteries in portable devices,electric vehicles,and energy storage systems has underscored the importance for accurately predicting their lifetimes.However,data scarcity often limits the accuracy of prediction models,which is escalated by the incompletion of data induced by the issues such as sensor failures.To address these challenges,we propose a novel approach to accommodate data insufficiency through achieving external information from incomplete data samples,which are usually discarded in existing studies.In order to fully unleash the prediction power of incomplete data,we have investigated the Multiple Imputation by Chained Equations(MICE)method that diversifies the training data through exploring the potential data patterns.The experimental results demonstrate that the proposed method significantly outperforms the baselines in the most considered scenarios while reducing the prediction root mean square error(RMSE)by up to 18.9%.Furthermore,we have also observed that the penetration of incomplete data benefits the explainability of the prediction model through facilitating the feature selection. 展开更多
关键词 Rechargeable batteries Battery lifetime prediction Data scarcity Incomplete data utilization
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Personalized Differential Privacy Graph Neural Network 认领 引用
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作者 Yanli Yuan Dian Lei +3 位作者 Chuan Zhang Zehui Xiong Chunhai Li Liehuang Zhu 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2026年第2期498-500,共3页
Dear Editor,This letter addresses the critical challenge of preserving privacy in graph learning without compromising on data utility.Differential privacy(DP)is emerging as an effective method for privacy-preserving g... Dear Editor,This letter addresses the critical challenge of preserving privacy in graph learning without compromising on data utility.Differential privacy(DP)is emerging as an effective method for privacy-preserving graph learning.However,its application often diminishes data utility,especially for nodes with fewer neighbors in graph neural networks(GNNs). 展开更多
关键词 graph neural networks gnns personalized differential privacy graph learning privacy preservation data utility preserving privacy graph neural network
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Preserving Data Privacy in Speech Data Publishing 认领 引用
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作者 SUN Jiaxin JIANG Jin ZHAO Ping 《Journal of Donghua University(English Edition)》 CAS 2020年第4期293-297,共5页
Speech data publishing breaches users'data privacy,thereby causing more privacy disclosure.Existing work sanitizes content,voice,and voiceprint of speech data without considering the consistence among these three ... Speech data publishing breaches users'data privacy,thereby causing more privacy disclosure.Existing work sanitizes content,voice,and voiceprint of speech data without considering the consistence among these three features,and thus is susceptible to inference attacks.To address the problem,we design a privacy-preserving protocol for speech data publishing(P3S2)that takes the corrections among the three factors into consideration.To concrete,we first propose a three-dimensional sanitization that uses feature learning to capture characteristics in each dimension,and then sanitize speech data using the learned features.As a result,the correlations among the three dimensions of the sanitized speech data are guaranteed.Furthermore,the(ε,δ)-differential privacy is used to theoretically prove both the data privacy preservation and the data utility guarantee of P3S2,filling the gap of algorithm design and performance evaluation.Finally,simulations on two real world datasets have demonstrated both the data privacy preservation and the data utility guarantee. 展开更多
关键词 speech data publishing data privacy data utility differential privacy
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Research on Utilization of Cost Data of Rail Transit Project Based on Database 认领 引用
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作者 WANGLei 《外文科技期刊数据库（文摘版）工程技术》 2022年第3期196-199,共4页
With the improvement of China's economic level and the increase of residents' income, the construction speed and scale of urban rail projects are also gradually improving and expanding. The modes of residents&... With the improvement of China's economic level and the increase of residents' income, the construction speed and scale of urban rail projects are also gradually improving and expanding. The modes of residents' travel in China are also gradually diversified, among which rail transportation is a necessary means of travel for long-distance and short-distance travel. No matter in order to ease the congestion of urban travel or for the safety of residents, attention must be paid to the quality of rail transit projects, and the important factor that determines the quality of rail transit projects is its own cost. Therefore, this paper will start from the database to study the cost data of rail transit projects, so as to promote the sustainable development of rail transit projects and help the development of China's transportation industry. 展开更多
关键词 database rail transit project cost data utilization study
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To Overcome the Obstacle of Data Exchange to Increase the Utilization Efficiency of Accounting Data 认领 引用
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《China Standardization》 2005年第1期10-11,共2页
On Nov.4th, AQSIQ (General Administration of Quality Supervision,Inspection and Quarantine of the People' s Republic of China), SAC (Standardization Administrationof China), National Audit Office of China (CNAO... On Nov.4th, AQSIQ (General Administration of Quality Supervision,Inspection and Quarantine of the People' s Republic of China), SAC (Standardization Administrationof China), National Audit Office of China (CNAO), and National Ministry of Finance of China jointlyheld the conference press on the national standard of Information Technology--Data Interface ofAccounting Software (GB/T 19581-2004) in Beijing. The standard was approved and issued on Sept. 20,2004 by AQSIQ and SAC, and it would come into effect all over the whole nation from January 1st,2005. Pu Changcheng, Vice Director of AQSIQ, Shi Aizhong, Vice Director of CNAO, Li Zhonghai. amember of the Party Group of AQSIQ and Director of SAC, the other leaders of concerned departmentssuch as National Ministry of Finance, National Telegraphy Office, and etc. attended the ConferencePress and made speeches. They fully affirmed the important significance and the achievements onstandardization work of electronic government business, and also they set new demands on the workfor the future. 展开更多
关键词 To Overcome the Obstacle of Data Exchange to Increase the Utilization Efficiency of Accounting Data SAC
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Differentially Private Multidimensional Data Publication 认领 引用 被引量：1
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作者 ZHANG Ji DONG Xin +3 位作者 YU Jiadi LUO Yuan LI Minglu WU Bin 《China Communications》 SCIE CSCD 2014年第A1期79-85,共7页
Multidimensional data provides enormous opportunities in a variety of applications. Recent research has indicated the failure of existing sanitization techniques （e.g., k-anonymity） to provide rigorous privacy guara... Multidimensional data provides enormous opportunities in a variety of applications. Recent research has indicated the failure of existing sanitization techniques （e.g., k-anonymity） to provide rigorous privacy guarantees. Privacy- preserving multidimensional data publishing currently lacks a solid theoretical foundation. It is urgent to develop new techniques with provable privacy guarantees, e-Differential privacy is the only method that can provide such guarantees. In this paper, we propose a multidimensional data publishing scheme that ensures c-differential privacy while providing accurate results for query processing. The proposed solution applies nonstandard wavelet transforms on the raw multidimensional data and adds noise to guarantee c-differential privacy. Then, the scheme processes arbitrarily queries directly in the noisy wavelet- coefficient synopses of relational tables and expands the noisy wavelet coefficients back into noisy relational tuples until the end result of the query. Moreover, experimental results demonstrate the high accuracy and effectiveness of our approach. 展开更多
关键词 data publication differential privacy data utility
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Differentially Private Support Vector Machines with Knowledge Aggregation 认领 引用
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作者 Teng Wang Yao Zhang +2 位作者 Jiangguo Liang Shuai Wang Shuanggen Liu 《Computers, Materials & Continua》 SCIE EI 2024年第3期3891-3907,共17页
With the widespread data collection and processing,privacy-preserving machine learning has become increasingly important in addressing privacy risks related to individuals.Support vector machine(SVM)is one of the most... With the widespread data collection and processing,privacy-preserving machine learning has become increasingly important in addressing privacy risks related to individuals.Support vector machine(SVM)is one of the most elementary learning models of machine learning.Privacy issues surrounding SVM classifier training have attracted increasing attention.In this paper,we investigate Differential Privacy-compliant Federated Machine Learning with Dimensionality Reduction,called FedDPDR-DPML,which greatly improves data utility while providing strong privacy guarantees.Considering in distributed learning scenarios,multiple participants usually hold unbalanced or small amounts of data.Therefore,FedDPDR-DPML enables multiple participants to collaboratively learn a global model based on weighted model averaging and knowledge aggregation and then the server distributes the global model to each participant to improve local data utility.Aiming at high-dimensional data,we adopt differential privacy in both the principal component analysis(PCA)-based dimensionality reduction phase and SVM classifiers training phase,which improves model accuracy while achieving strict differential privacy protection.Besides,we train Differential privacy(DP)-compliant SVM classifiers by adding noise to the objective function itself,thus leading to better data utility.Extensive experiments on three high-dimensional datasets demonstrate that FedDPDR-DPML can achieve high accuracy while ensuring strong privacy protection. 展开更多
关键词 Differential privacy support vector machine knowledge aggregation data utility
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Implicit privacy preservation:a framework based on data generation 认领 引用
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作者 Qing Yang Cheng Wang +2 位作者 Teng Hu Xue Chen Changjun Jiang 《Security and Safety》 2023年第1期45-62,共18页
This paper addresses a special and imperceptible class of privacy,called implicit privacy.In contrast to traditional(explicit)privacy,implicit privacy has two essential prop-erties:(1)It is not initially defined as a ... This paper addresses a special and imperceptible class of privacy,called implicit privacy.In contrast to traditional(explicit)privacy,implicit privacy has two essential prop-erties:(1)It is not initially defined as a privacy attribute;(2)it is strongly associated with privacy attributes.In other words,attackers could utilize it to infer privacy attributes with a certain probability,indirectly resulting in the disclosure of private information.To deal with the implicit privacy disclosure problem,we give a measurable definition of implicit privacy,and propose an ex-ante implicit privacy-preserving framework based on data generation,called IMPOSTER.The framework consists of an implicit privacy detection module and an implicit privacy protection module.The former uses normalized mutual information to detect implicit privacy attributes that are strongly related to traditional privacy attributes.Based on the idea of data generation,the latter equips the Generative Adversarial Network(GAN)framework with an additional discriminator,which is used to eliminate the association between traditional privacy attributes and implicit ones.We elaborate a theoretical analysis for the convergence of the framework.Experiments demonstrate that with the learned gen-erator,IMPOSTER can alleviate the disclosure of implicit privacy while maintaining good data utility. 展开更多
关键词 Privacy preservation Implicit privacy Generative adversarial network Data utility Data generation
Data Sharing and Innovative Utilization:A Study on Shenzhen's Multi-Actor Interaction Mechanism of Smart City Construction 认领 引用
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作者 He Jing Yuan Xiaohui +2 位作者 Zhou Kai Guo Chen Li Caige 《China City Planning Review》 CSCD 2021年第4期33-42,共10页
Data is not only a key production factor but also an important foundation and strategic resource that drives economic growth and social progress in the era of digital economy. Data sharing and innovative utilization i... Data is not only a key production factor but also an important foundation and strategic resource that drives economic growth and social progress in the era of digital economy. Data sharing and innovative utilization in an ethical and responsible manner is a focus of the current studies on smart city construction. Taking Shenzhen as an example, this paper analyzes the three typical cases of data legislation, data sharing and utilization,and data-based anti-epidemic action in its smart city construction and explores the respective role of the four actors of the government, enterprises,research institutes, and the public in innovating data utilization to serve the public interests through data sharing. By studying Shenzhen’s multi-actor interaction mechanism of smart city construction, the paper tries to provide a useful experience for the construction of smart cities in China from the perspectives of data management, data sharing, and innovative data utilization. 展开更多
关键词 data sharing data utilization sharing city smart city multi-actor interaction mechanism data factor market Shenzhen
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