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Privacy Computing: Concept, Computing Framework, and Future Development Trends Article

Fenghua Li, Hui Li, Ben Niu, Jinjun Chen

Engineering 2019, Volume 5, Issue 6,   Pages 1179-1192 doi: 10.1016/j.eng.2019.09.002

Abstract: private information among different information systems and the difficulty of tracing the source of privacyTherefore, existing privacy-preserving schemes cannot provide systematic privacy preservation.We then propose a theory of privacy computing and a key technology system that includes a privacy computingframework, a formal definition of privacy computing, four principles that should be followed in privacycomputing, algorithm design criteria, evaluation of the privacy-preserving effect, and a privacy computing

Keywords: Privacy computing     Private information description     Privacy metric     Evaluation of the privacy-preservingeffect     Privacy computing language    

Large-scale App privacy governance

Frontiers of Engineering Management   Pages 640-652 doi: 10.1007/s42524-022-0228-y

Abstract: Currently, the government mainly filters out Apps with potential privacy problems manually.We introduce Privacy Level (P-Level) to measure an App’s probability of leaking privacy.P-Level is calculated on the basis of Permission-based Privacy Value (P-Privacy) and Usage-based PrivacyValue (U-Privacy).Through P-Privacy, U-Privacy, and P-Level, potentially problematic Apps can be filtered out efficiently

Keywords: privacy risk     Privacy Level     quantification     large-scale App governance    

Face recognition based on subset selection via metric learning on manifold

Hong SHAO,Shuang CHEN,Jie-yi ZHAO,Wen-cheng CUI,Tian-shu YU

Frontiers of Information Technology & Electronic Engineering 2015, Volume 16, Issue 12,   Pages 1046-1058 doi: 10.1631/FITEE.1500085

Abstract: In this paper, we employ a metric learning approach which helps find the active elements correctly byAfter the metric has been learned, a neighborhood graph is constructed in the projected space.

Keywords: Face recognition     Sparse representation     Manifold structure     Metric learning     Subset selection    

Low-Cost Federated Broad Learning for Privacy-Preserved Knowledge Sharing in the RIS-Aided Internet of

Xiaoming Yuan,Jiahui Chen,Ning Zhang,Qiang Ye,Changle Li,Chunsheng Zhu,Xuemin Sherman Shen,

Engineering doi: 10.1016/j.eng.2023.04.015

Abstract: However, it is challenging to ensure high efficiency of local data learning models while preventing privacyIn order to protect data privacy and improve data learning efficiency in knowledge sharing, we propose

Keywords: Knowledge sharing     Internet of Vehicles     Federated learning     Broad learning     Reconfigurable intelligent surfaces     Resource allocation    

Toward Privacy-Preserving Personalized Recommendation Services Review

Cong Wang, Yifeng Zheng, Jinghua Jiang, Kui Ren

Engineering 2018, Volume 4, Issue 1,   Pages 21-28 doi: 10.1016/j.eng.2018.02.005

Abstract: Therefore, it is of paramount importance to develop practical privacy-preserving techniques to maintainthe intelligence of personalized recommendation services while respecting user privacy.We present the general architecture of personalized recommendation systems, the privacy issues therein, and existing works that focus on privacy-preserving personalized recommendation services.protection, and thoroughly discuss and compare their merits and demerits, especially in terms of privacy

Keywords: Privacy protection     Personalized recommendation services     Targeted delivery     Collaborative filtering     Machine    

A lightweight authentication scheme with user untraceability

Kuo-Hui YEH

Frontiers of Information Technology & Electronic Engineering 2015, Volume 16, Issue 4,   Pages 259-271 doi: 10.1631/FITEE.1400232

Abstract: With the rapid growth of electronic commerce and associated demands on variants of Internet based applications, application systems providing network resources and business services are in high demand around the world. To guarantee robust security and computational efficiency for service retrieval, a variety of authentication schemes have been proposed. However, most of these schemes have been found to be lacking when subject to a formal security analysis. Recently, Chang (2014) introduced a formally provable secure authentication protocol with the property of user-untraceability. Unfortunately, based on our analysis, the proposed scheme fails to provide the property of user-untraceability as claimed, and is insecure against user impersonation attack, server counterfeit attack, and man-in-the-middle attack. In this paper, we demonstrate the details of these malicious attacks. A security enhanced authentication scheme is proposed to eliminate all identified weaknesses.

Keywords: Authentication     Privacy     Security     Smart card     Untraceability    

Privacy and security federated reference architecture for Internet of Things Position Paper

Musab KAMAL, Imran RASHID, Waseem IQBAL, Muhammad Haroon SIDDIQUI, Sohaib KHAN, Ijaz AHMAD,waseem.iqbal@mcs.edu.pk

Frontiers of Information Technology & Electronic Engineering 2023, Volume 24, Issue 4,   Pages 481-508 doi: 10.1631/FITEE.2200368

Abstract: This has given rise to risks associated with the privacy and security of systems.To counter these issues, we need to implement privacy and security right from the building blocks ofThis emphasizes the need to standardize and organize the IoT reference architecture in federation with privacyWe propose an architecture, the privacy-federated IoT security reference architecture (PF-IoT-SRA), whichinterprets all the involved privacy metrics and counters major threats and attacks in the IoT communication

Keywords: Architecture trade-off analysis method (ATAM)     Internet architecture board     Internet of Things (IoT)     Privacyenhancing technologies     Privacy validation chain    

SRIM Scheme: An Impression-Management Scheme for Privacy-Aware Photo-Sharing Users Article

Fenghua Li, Zhe Sun, Ben Niu, Yunchuan Guo, Ziwen Liu

Engineering 2018, Volume 4, Issue 1,   Pages 85-93 doi: 10.1016/j.eng.2018.02.003

Abstract: However, most of the existing privacy-aware solutions have two main drawbacks: ① Users must decide manuallythis paper, we propose a social relation impression-management (SRIM) scheme to protect relational privacy

Keywords: Impression management     Relational privacy     Photo sharing     Policy recommendation     Proxemics    

A software defect prediction method with metric compensation based on feature selection and transfer Research Article

Jinfu CHEN, Xiaoli WANG, Saihua CAI, Jiaping XU, Jingyi CHEN, Haibo CHEN,caisaih@ujs.edu.cn

Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 5,   Pages 715-731 doi: 10.1631/FITEE.2100468

Abstract: training efficiency and thus decrease the prediction accuracy of the model; (2) the distribution of metricbetter results on area under the receiver operating characteristic curve (AUC) value and F1-measure metric

Keywords: Defect prediction     Feature selection     Transfer learning     Metric compensation    

A comprehensive review and analysis of solar forecasting techniques

Pardeep SINGLA, Manoj DUHAN, Sumit SAROHA

Frontiers in Energy 2022, Volume 16, Issue 2,   Pages 187-223 doi: 10.1007/s11708-021-0722-7

Abstract: In the last two decades, renewable energy has been paid immeasurable attention to toward the attainment of electricity requirements for domestic, industrial, and agriculture sectors. Solar forecasting plays a vital role in smooth operation, scheduling, and balancing of electricity production by standalone PV plants as well as grid interconnected solar PV plants. Numerous models and techniques have been developed in short, mid and long-term solar forecasting. This paper analyzes some of the potential solar forecasting models based on various methodologies discussed in literature, by mainly focusing on investigating the influence of meteorological variables, time horizon, climatic zone, pre-processing techniques, air pollution, and sample size on the complexity and accuracy of the model. To make the paper reader-friendly, it presents all-important parameters and findings of the models revealed from different studies in a tabular mode having the year of publication, time resolution, input parameters, forecasted parameters, error metrics, and performance. The literature studied showed that ANN-based models outperform the others due to their nonlinear complex problem-solving capabilities. Their accuracy can be further improved by hybridization of the two models or by performing pre-processing on the input data. Besides, it also discusses the diverse key constituents that affect the accuracy of a model. It has been observed that the proper selection of training and testing period along with the correlated dependent variables also enhances the accuracy of the model.

Keywords: forecasting techniques     hybrid models     neural network     solar forecasting     error metric     support vector machine    

Preserving privacy information flowsecurity in composite service evolution None

Huan-feng PENG, Zhi-qiu HUANG, Lin-yuan LIU, Yong LI, Da-juan FAN, Yu-qing WANG

Frontiers of Information Technology & Electronic Engineering 2018, Volume 19, Issue 5,   Pages 626-638 doi: 10.1631/FITEE.1700359

Abstract: After a composite service is deployed, user privacy requirements and trust levels of component servicesWhen the changes occur, it is critical to preserve privacy information flow security.First, a privacy data item dependency analysis method based on a Petri net model is presented.Then the set of privacy data items collected by each component service is derived through a privacy dataFinally, the evolution operations that preserve privacy information flow security are defined.

Keywords: Composite service     Privacy information flow security     Service evolution     Petri net    

Calculation of the Behavior Utility of a Network System: Conception and Principle Article

Changzhen Hu

Engineering 2018, Volume 4, Issue 1,   Pages 78-84 doi: 10.1016/j.eng.2018.02.010

Abstract:

The service and application of a network is a behavioral process that is oriented toward its operations and tasks, whose metrics and evaluation are still somewhat of a rough comparison. This paper describes scenes of network behavior as differential manifolds. Using the homeomorphic transformation of smooth differential manifolds, we provide a mathematical definition of network behavior and propose a mathematical description of the network behavior path and behavior utility. Based on the principle of differential geometry, this paper puts forward the function of network behavior and a calculation method to determine behavior utility, and establishes the calculation principle of network behavior utility. We also provide a calculation framework for assessment of the network’s attack-defense confrontation on the strength of behavior utility. Therefore, this paper establishes a mathematical foundation for the objective measurement and precise evaluation of network behavior.

Keywords: Network metric evaluation     Differential manifold     Network behavior utility     Network attack-defense confrontation    

Towards a respondent-preferred ki-anonymity model

Kok-Seng WONG,Myung Ho KIM

Frontiers of Information Technology & Electronic Engineering 2015, Volume 16, Issue 9,   Pages 720-731 doi: 10.1631/FITEE.1400395

Abstract: Recently, privacy concerns about data collection have received an increasing amount of attention.We believe that this assumption is not realistic because the increase in privacy concerns causes some

Keywords: Anonymous data collection     Respondent-preferred privacy protection     k-anonymity    

Data-Driven Learning for Data Rights, Data Pricing, and Privacy Computing Review

Jimin Xu, Nuanxin Hong, Zhening Xu, Zhou Zhao, Chao Wu, Kun Kuang, Jiaping Wang, Mingjie Zhu, Jingren Zhou, Kui Ren, Xiaohu Yang, Cewu Lu, Jian Pei, Harry Shum

Engineering 2023, Volume 25, Issue 6,   Pages 66-76 doi: 10.1016/j.eng.2022.12.008

Abstract: dedicated to the issue of complying with data regulations and other data-transaction issues such as privacytopic, this review covers the three key issues of data transaction: data rights, data pricing, and privacybeneficial to human society, AI algorithms will then be assessed by data protection regulations (i.e., privacy

Keywords: Data science     Artificial intelligence     Data rights     Data pricing     Privacy computing    

An intuitive general rank-based correlation coefficient Research Articles

Divya PANDOVE, Shivani GOEL, Rinkle RANI

Frontiers of Information Technology & Electronic Engineering 2018, Volume 19, Issue 6,   Pages 699-711 doi: 10.1631/FITEE.1601549

Abstract: We propose a predictive metric to calculate correlations between paired values, known as the generalIt fulfills the five basic criteria of a predictive metric: independence from sample size, value betweenFurthermore, the metric has been validated by performing experiments using a real-time dataset and randomThe comparison results show that the proposed metric fares better than the existing metric on all thepredictive metric criteria.

Keywords: General rank-based correlation coefficient     Multivariate analysis     Predictive metric     Spearman’s rank correlation    

Title Author Date Type Operation

Privacy Computing: Concept, Computing Framework, and Future Development Trends

Fenghua Li, Hui Li, Ben Niu, Jinjun Chen

Journal Article

Large-scale App privacy governance

Journal Article

Face recognition based on subset selection via metric learning on manifold

Hong SHAO,Shuang CHEN,Jie-yi ZHAO,Wen-cheng CUI,Tian-shu YU

Journal Article

Low-Cost Federated Broad Learning for Privacy-Preserved Knowledge Sharing in the RIS-Aided Internet of

Xiaoming Yuan,Jiahui Chen,Ning Zhang,Qiang Ye,Changle Li,Chunsheng Zhu,Xuemin Sherman Shen,

Journal Article

Toward Privacy-Preserving Personalized Recommendation Services

Cong Wang, Yifeng Zheng, Jinghua Jiang, Kui Ren

Journal Article

A lightweight authentication scheme with user untraceability

Kuo-Hui YEH

Journal Article

Privacy and security federated reference architecture for Internet of Things

Musab KAMAL, Imran RASHID, Waseem IQBAL, Muhammad Haroon SIDDIQUI, Sohaib KHAN, Ijaz AHMAD,waseem.iqbal@mcs.edu.pk

Journal Article

SRIM Scheme: An Impression-Management Scheme for Privacy-Aware Photo-Sharing Users

Fenghua Li, Zhe Sun, Ben Niu, Yunchuan Guo, Ziwen Liu

Journal Article

A software defect prediction method with metric compensation based on feature selection and transfer

Jinfu CHEN, Xiaoli WANG, Saihua CAI, Jiaping XU, Jingyi CHEN, Haibo CHEN,caisaih@ujs.edu.cn

Journal Article

A comprehensive review and analysis of solar forecasting techniques

Pardeep SINGLA, Manoj DUHAN, Sumit SAROHA

Journal Article

Preserving privacy information flowsecurity in composite service evolution

Huan-feng PENG, Zhi-qiu HUANG, Lin-yuan LIU, Yong LI, Da-juan FAN, Yu-qing WANG

Journal Article

Calculation of the Behavior Utility of a Network System: Conception and Principle

Changzhen Hu

Journal Article

Towards a respondent-preferred ki-anonymity model

Kok-Seng WONG,Myung Ho KIM

Journal Article

Data-Driven Learning for Data Rights, Data Pricing, and Privacy Computing

Jimin Xu, Nuanxin Hong, Zhening Xu, Zhou Zhao, Chao Wu, Kun Kuang, Jiaping Wang, Mingjie Zhu, Jingren Zhou, Kui Ren, Xiaohu Yang, Cewu Lu, Jian Pei, Harry Shum

Journal Article

An intuitive general rank-based correlation coefficient

Divya PANDOVE, Shivani GOEL, Rinkle RANI

Journal Article