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Differentially Private Multidimensional data publication

China Communications 2014年第A01期11卷 79-85页

作者： ZHANG Ji DONG Xin YU Jiadi LUO Yuan LI Minglu WU Bin SchoolofMathematicalSciences FudanUniversityShanghai201203China DepartmentofComputerScienceandEngineering ShanghaiJiaoTongUniversityShanghai200240China InformationSecurityLaboratory NationalDisasterRecoveryTechnologyEngineeringLa-boratoryBeijingUniversityofPostaandTelecommunicationBeijing100876China

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.

关键词： data publication differential privacy data utility

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An Optimized Sanitization Approach for Minable data publication

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Big data Mining and Analytics 2022年第3期5卷 257-269页

作者： Fan Yang Xiaofeng Liao College of Computer Science Chongqing UniversityChongqing 400044China

Minable data publication is ubiquitous since it is beneficial to sharing/trading data among commercial companies and further facilitates the development of data-driven ***,the minable data publication is often implemented by publishers with limited privacy concerns such that the published dataset is minable by malicious *** prohibits minable data publication since the published data may contain sensitive ***,it is urgently demanded to present some approaches and technologies for reducing the privacy leakage *** this end,in this paper,we propose an optimized sanitization approach for minable data publication(named as SA-MDP).SA-MDP supports association rules mining function while providing privacy protection for specific *** SA-MDP,we consider the trade-off between the data utility and the data privacy in the minable data publication *** address this problem,SA-MDP designs a customized particle swarm optimization(PSO)algorithm,where the optimization objective is determined by both the data utility and the data ***,we take advantage of PSO to produce new particles,which is achieved by random mutation or learning from the best ***,SA-MDP can avoid the solutions being trapped into local ***,we design a proper fitness function to guide the particles to run towards the optimal ***,we present a preprocessing method before the evolution process of the customized PSO algorithm to improve the convergence ***,the proposed SA-MDP approach is performed and verified over several *** experimental results have demonstrated the effectiveness and efficiency of SA-MDP.

关键词： data publication data sanitization association rules hiding evolutionary algorithm

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Graph publishing method based on differential privacy protection

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High Technology Letters 2018年第2期24卷 134-141页

作者： Wang Junli Yang Li Wu Yuxi Guan Min Department of Computer Science and Technology Tongji University Shanghai 201804 P. R. China

There are growing concerns surrounding the data security of social networks because large amount of user information and sensitive data are collected. Differential privacy is an effective method for privacy protection that can provide rigorous and quantitative protection. Concerning the application of differential privacy in social networks,this paper analyzes current trends of research and provides some background information including privacy protection standards and noise *** on the privacy protection of social network data publishing,a graph-publishing model is designed to provide differential privacy in social networks via three steps: Firstly,according to the features of social network where two nodes that possess certain common properties are associated with a higher probability,a raw graph is divided into several disconnected sub-graphs,and correspondingly dense adjacent matrixes and the number of bridges are obtained. Secondly,taking the advantage of quad-trees,dense region exploration of the adjacent matrixes is conducted. Finally,using an exponential mechanism and leaf nodes of quad-trees,an adjacent matrix of the sanitized graph is reconstructed. In addition,a set of experiments is conducted to evaluate its feasibility,availability and strengths using three analysis techniques: degree distribution,shortest path,and clustering coefficients.

关键词： differential privacy social network data publication

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Different strategies for differentially private histogram publication

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Journal of Communications and Information Networks 2017年第3期2卷 68-77页

作者： Xue Meng Hui Li Jiangtao Cui School of Cyber Engineering Xidian UniversityXi'an 710071China School of Computer Science and Technology Xidian UniversityXi'an 710071China

Differential privacy is a strong notion for protecting individual privacy in data analysis or publication,with strong privacy guaranteeing security against adversaries with arbitrary background knowledge.A histogram is a representative and popular tool for data publication and visualization *** the emergence and development of data analysis and increasing release demands,protecting the private data and preventing sensitive information from leakage has become one of the major challenges for histogram *** recent years,many approaches have been proposed for publishing histograms with differential *** paper explores the problem of publishing histograms with differential privacy,and provides a systematical summarization of existing research efforts in this field,begining with a discussion of the basic principles and characteristics of the ***,we provide a comprehensive comparison of a series of state-of-the-art histogram publication ***,we provide possible suggestions for further expansions of future work in this area.

关键词： differential privacy histogram Laplacian noise data publication accuracy utility

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基于数据的学校决策：本质、过程及其实践问题

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中小学学校管理 2019年第6期

作者：杨帆贺利林

基于数据的学校决策，在本质上是学校持续的、系统化、协同性地利用数据做出判断的过程。学校利用数据进行决策有两种不同的过程，一种以清晰呈现的目标或过程为导向，强调利用数据清晰地定义问题和最终进行学校改进的评估；另一种以发... 详细信息

基于数据的学校决策，在本质上是学校持续的、系统化、协同性地利用数据做出判断的过程。学校利用数据进行决策有两种不同的过程，一种以清晰呈现的目标或过程为导向，强调利用数据清晰地定义问题和最终进行学校改进的评估；另一种以发现问题为导向，强调将数据作为促进各方讨论的支点，进而建立数据驱动的学校文化建设。对于学校而言，成功地使用数据依赖于对以下几个方面问题的解决：数据的代表性问题、数据解读的“多样化”问题、第三方机构及数据顾问的角色定位问题、“不利”数据的公开问题、数据驱动学校持续改进的问题。

关键词：数据学校决策持续改进数据公开第三方机构 data school decision constant improvement data publication the third party

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数据论文的出现与发展

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图书馆学情报学 2016年第4期

作者：屈宝强王凯

数据论文是一种新型的学术出版物形式，它通过描述创建和处理数据方法的细节、数据的结构和格式、数据再利用的潜力等内容促进数据开发和利用。文章对数据论文的概念内涵、基本特征、表现形式、主要内容以及出版流程等进行了介绍，以期... 详细信息

数据论文是一种新型的学术出版物形式，它通过描述创建和处理数据方法的细节、数据的结构和格式、数据再利用的潜力等内容促进数据开发和利用。文章对数据论文的概念内涵、基本特征、表现形式、主要内容以及出版流程等进行了介绍，以期推动我国数据论文出版的发展。

关键词：数据共享数据论文数据出版数据期刊 data sharing data paper data publication data journal

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