检索结果-南通市图书馆

Background knowledge based privacy metric model for online social networks

The Journal of China Universities of Posts and Telecommunications 2014年第2期21卷 75-82页

作者： CHENG Cheng ZHANG Chun-hong JI Yang School of Information and Communication Engineering Beijing University of Posts and Telecommunications

The data of online social network （OSN） is collected currently by the third party for various purposes. One of the problems in such practices is how to measure the privacy breach to assure users. The recent work on OSN privacy is mainly focus on privacy-preserving data publishing. However, the work on privacy metric is not systematic but mainly focus on the traditional datasets. Compared with the traditional datasets, the attribute types in OSN are more diverse and the tuple is relevant to each other. The retweet and comment make the graph character of OSN notably. Furthermore, the open application programming interfaces （APIs） and lower register barrier make OSN open environment, in which the background knowledge is more easily achieved by adversaries. This paper analyzes the background knowledge in OSN and discusses its characteristics in detail. Then a privacy metric model faces OSN background knowledge based on kernel regression is proposed. In particular, this model takes the joint attributes and link knowledge into consideration. The effect of different data distributions is discussed. The real world data set from *** has been adopted. It is demonstrated that the privacy metric algorithm in this article is effective in OSN privacy evaluation. The prediction error is 30% lower than that of the work mentioned above

关键词： privacy metric social network background knowledge kernel regression

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

privacy Quantification Model Based on the Bayes Conditional Risk in Location-Based Services

引用

Tsinghua Science and Technology 2014年第5期19卷 452-462页

作者： Xuejun Zhang Xiaolin Gui Feng Tian Si Yu Jian An Department of Computer Science and Technology the Shaanxi Province Key Laboratory of Computer NetworkXi’an Jiaotong University School of Electronic and Information Engineering Lanzhou Jiaotong University

The widespread use of Location-Based Services （LBSs）, which allows untrusted service providers to collect large quantities of information regarding users＇ locations, has raised serious privacy concerns. In response to these issues, a variety of LBS privacy Protection Mechanisms （LPPMs） have been recently proposed. However, evaluating these LPPMs remains problematic because of the absence of a generic adversarial model for most existing privacy metrics. In particular, the relationships between these metrics have not been examined in depth under a common adversarial model, leading to a possible selection of the inappropriate metric, which runs the risk of wrongly evaluating LPPMs. In this paper, we address these issues by proposing a privacy quantification model, which is based on Bayes conditional privacy, to specify a general adversarial model. This model employs a general definition of conditional privacy regarding the adversary＇s estimation error to compare the different LBS privacy metrics. Moreover, we present a theoretical analysis for specifying how to connect our metric with other popular LBS privacy metrics. We show that our privacy quantification model permits interpretation and comparison of various popular LBS privacy metrics under a common perspective. Our results contribute to a better understanding of how privacy properties can be measured, as well as to the better selection of the most appropriate metric for any given LBS application.

关键词： location-based services Bayes decision estimator privacy metric adversarial mode

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

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

引用

Engineering 2019年第6期5卷 1179-1192页

作者： Fenghua Li Hui Li Ben Niu Jinjun Chen Institute of Information Engineering Chinese Academy of SciencesBeijing 100093China School of Cyber Security University of Chinese Academy of SciencesBeijing 100049China State Key Laboratory of Integrated Services Networks School of Cyber EngineeringXidian UniversityXi’an 710071China Department of Computer Science and Software Engineering Swinburne University of TechnologyHawthornVIC 3122Australia

With the rapid development of information technology and the continuous evolution of personalized ser- vices, huge amounts of data are accumulated by large internet companies in the process of serving users. Moreover, dynamic data interactions increase the intentional/unintentional persistence of private infor- mation in different information systems. However, problems such as the cask principle of preserving pri- vate information among different information systems and the dif culty of tracing the source of privacy violations are becoming increasingly serious. Therefore, existing privacy-preserving schemes cannot pro- vide systematic privacy preservation. In this paper, we examine the links of the information life-cycle, such as information collection, storage, processing, distribution, and destruction. We then propose a the- ory of privacy computing and a key technology system that includes a privacy computing framework, a formal de nition of privacy computing, four principles that should be followed in privacy computing, ffect algorithm design criteria, evaluation of the privacy-preserving effect, and a privacy computing language. Finally, we employ four application scenarios to describe the universal application of privacy computing, and discuss the prospect of future research trends. This work is expected to guide theoretical research on user privacy preservation within open environments.

关键词： privacy computing Private information description privacy metric Evaluation of the privacy-preserving effect privacy computing language

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

privacy-friendly spatial crowdsourcing in vehic-ular networks

引用

Journal of Communications and Information Networks 2017年第2期2卷 59-74页

作者： Cheng Huang Rongxing Lu Hui Zhu Department of Electrical and Computer Engineering University of WaterlooWaterloo ON N2L 3G1Canada Faculty of Computer Science University of New BrunswickFredericton NB E3B 5A3Canada School of Cyber Engineering Xidian UniversityXi'an 710126China

With the evolution of conventional VANETs(Vehicle Ad-hoc Networks)into the IoV(Internet of Vehicles),vehicle-based spatial crowdsourcing has become a potential solution for crowdsourcing *** vehicular networks,a spatial-temporal task/question can be outsourced(i.e.,task/question relating to a particular location and in a speci c time period)to some suitable smart vehicles(also known as workers)and then these workers can help solve the task/***,an inevitable barrier to the widespread deployment of spatial crowdsourcing applications in vehicular networks is the concern of ***,We propose a novel privacy-friendly spatial crowdsourcing *** the existing schemes,the proposed scheme considers the privacy issue from a new perspective according that the spatial-temporal tasks can be linked and analyzed to break the location privacy of *** cally,to address the challenge,three privacy requirements(***,untraceability,and unlinkability)are de ned and the proposed scheme combines an effcient anonymous technique with a new composite privacy metric to protect against *** privacy analyses show that the proposed scheme is *** addition,performance evaluations via extensive simulations are also conducted,and the results demonstrate the effciency and e ectiveness of the proposed scheme.

关键词： spatial crowdsourcing vehicular networks location privacy IoV(Internet of Vehicles) privacy metric

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

欢迎您,

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

在线全文

在线全文

请选择保存的检索档案：

请选择收藏分类：

通借通还

欢迎您,

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

在线全文

在线全文

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：