检索结果-内蒙古大学图书馆

您好，读者！请登录

内蒙古大学图书馆

首页
概况
党建
资源
服务
科研支持
- 论文收录引用证明
- 科技查新
知识产权
档案馆
帮助

咨询与建议

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

您的常用邮箱：*

您的手机号码：*

问题描述：

当前已输入0个字，您还可以输入200个字

全部搜索
期刊论文
图书
学位论文
标准
纸本馆藏
外文资源发现
数据库导航
超星发现

高级检索

时间限定

出版年份：

文献类型

图书期刊文献学位论文多媒体

馆藏选择

电子馆藏纸本馆藏

核心期刊

全部期刊 SCI 收录期刊 SSCI 收录期刊 EI 收录期刊 CSCD 收录期刊 CSSCI 收录期刊

语言

中文英文

文献类型

期刊文献图书学位论文标准纸本馆藏

帮助

文字说明：

T=题名（书名、题名），A=作者（责任者），K=主题词，P=出版物名称，PU=出版社名称，O=机构（作者单位、学位授予单位、专利申请人），L=中图分类号，C=学科分类号，U=全部字段，Y=年（出版发行年、学位年度、标准发布年）

检索规则说明：

AND代表“并且”；OR代表“或者”；NOT代表“不包含”；(注意必须大写,运算符两边需空一格)

检索范例：

范例一：(K=图书馆学 OR K=情报学) AND A=范并思 AND Y=1982-2016
范例二：P=计算机应用与软件 AND (U=C++ OR U=Basic) NOT K=Visual AND Y=2011-2016

分类表

所选分类

>> <<

限定检索结果

文献类型

119 篇 会议
6 册 图书
5 篇 期刊文献

馆藏范围

130 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

106 篇 工学
- 102 篇 计算机科学与技术...
- 12 篇 软件工程
- 8 篇 电气工程
- 7 篇 信息与通信工程
- 4 篇 电子科学与技术（可...
- 2 篇 控制科学与工程
- 1 篇 动力工程及工程热...
- 1 篇 建筑学
- 1 篇 土木工程
- 1 篇 测绘科学与技术
- 1 篇 安全科学与工程
- 1 篇 网络空间安全
36 篇 理学
- 29 篇 统计学（可授理学、...
- 14 篇 数学
- 2 篇 系统科学
- 1 篇 物理学
6 篇 管理学
- 3 篇 管理科学与工程(可...
- 3 篇 图书情报与档案管...
- 1 篇 工商管理
3 篇 法学
- 3 篇 社会学

主题

21 篇 differential pri...
18 篇 statistical disc...
14 篇 privacy
13 篇 synthetic data
11 篇 data privacy
11 篇 confidentiality
6 篇 statistical disc...
4 篇 data utility
4 篇 cell suppression
3 篇 clustering
3 篇 tabular data
3 篇 computers and so...
3 篇 privacy-preservi...
3 篇 statistics
3 篇 microaggregation
3 篇 database managem...
3 篇 disclosure contr...
3 篇 official statist...
3 篇 controlled tabul...
3 篇 additive noise

机构

3 篇 natl ctr hlth st...
3 篇 inst employment ...
3 篇 penn state univ ...
3 篇 umea univ dept c...
2 篇 univ politecn ca...
2 篇 ctr wiskunde & i...
2 篇 urban inst washi...
2 篇 csiro computat i...
2 篇 georgia southern...
2 篇 eurecom biot
2 篇 univ rovira & vi...
2 篇 stat netherlands...
2 篇 off natl stat ti...
2 篇 carnegie mellon ...
2 篇 department of co...
2 篇 univ politecn ca...
2 篇 rovira i virgili...
2 篇 rti int res tria...
2 篇 univ lancaster l...
2 篇 karlsruhe inst t...

作者

14 篇 domingo-ferrer j...
6 篇 muralidhar krish...
5 篇 oganian anna
5 篇 josep domingo-fe...
4 篇 castro jordi
4 篇 sanchez david
4 篇 vilhuber lars
3 篇 slavkovic aleksa...
3 篇 ruggles steven
3 篇 drechsler joerg
3 篇 ruiz nicolas
3 篇 blanco-justicia ...
3 篇 lesaja goran
3 篇 torra vicenc
3 篇 giessing sarah
3 篇 iacob ionut
2 篇 mccaa robert
2 篇 onen melek
2 篇 zari oualid
2 篇 soria-comas jord...

语言

129 篇 英文
1 篇 中文

检索条件"任意字段=International Conference on Privacy in Statistical Databases, PSD 2012"

共 130 条记录，以下是61-70 订阅

全选清除本页清除全部题录导出标记到"检索档案"

详细简洁

排序：

Multivariate Top-Coding for statistical Disclosure Limitation

Multivariate Top-Coding for Statistical Disclosure Limitatio...

引用

international conference on privacy in statistical databases (psd)

作者： Oganian, Anna Iacob, Ionut Lesaja, Goran Natl Ctr Hlth Stat 3311 Toledo Rd Hyattsville MD 20782 USA Georgia Southern Univ Dept Math Sci POB 8093 Statesboro GA 30460 USA US Naval Acad Math Dept 121 Blake Rd Annapolis MD 21402 USA

ISBN: (纸本)9783030575205;9783030575212

One of the most challenging problems for national statistical agencies is how to release to the public microdata sets with a large number of attributes while keeping the disclosure risk of sensitive information of data subjects under control. When statistical agencies alter microdata in order to limit the disclosure risk, they need to take into account relationships between the variables to produce a good quality public data set. Hence, statistical Disclosure Limitation (SDL) methods should not be univariate (treating each variable independently of others), but preferably multivariate, that is, handling several variables at the same time. statistical agencies are often concerned about disclosure risk associated with the extreme values of numerical variables. Thus, such observations are often top or bottom-coded in the public use files. Top-coding consists of the substitution of extreme observations of the numerical variable by a threshold, for example, by the 99th percentile of the corresponding variable. Bottom coding is defined similarly but applies to the values in the lower tail of the distribution. We argue that a univariate form of top/bottom-coding may not offer adequate protection for some subpopulations which are different in terms of a top-coded variable from other subpopulations or the whole population. In this paper, we propose a multivariate form of top-coding based on clustering the variables into groups according to some metric of closeness between the variables and then forming the rules for the multivariate top-codes using techniques of Association Rule Mining within the clusters of variables obtained on the previous step. Bottom-coding procedures can be defined in a similar way. We illustrate our method on a genuine multivariate data set of realistic size.

关键词： statistical disclosure limitation (SDL) Top-coding Hierarchical clustering Association Rule Mining Dimensionality reduction Genetic algorithm

来源：评论

学校读者我要写书评

暂无评论

international conference on privacy in statistical databases, psd 2016

引用

international conference on privacy in statistical databases, psd 2016

ISBN: (纸本)9783319453804

The proceedings contain 19 papers. The special focus in this conference is on Tabular Data Protection, Microdata, Big Data Masking, Protection Using privacy Models and Synthetic Data. The topics include: An alternative framework of sensitivity measures;empirical analysis of sensitivity rules;cells with frequency exceeding 10 that should be suppressed based on descriptive statistics;a second order cone formulation of continuous CTA model;anonymization in the time of big data;propensity score based conditional group swapping for disclosure limitation of strata-defining variables;a rule-based approach to local anonymization for exclusivity handling in statistical databases;perturbative data protection of multivariate nominal datasets;spatial smoothing and statistical disclosure control;on-average KL-privacy and its equivalence to generalization for max-entropy mechanisms;synthetic microdata from the perspective of distribution type;a synthetic data generator for testing anonymization techniques;towards a national remote access system for register-based research;accurate estimation of structural equation models with remote partitioned data;a new algorithm for protecting aggregate business microdata via a remote system;rank-based record linkage for re-identification risk assessment;computational issues in the design of transition probabilities and disclosure risk estimation for additive noise and enabling collaborative privacy in user-generated emergency reports.

关键词：

来源：评论

学校读者我要写书评

暂无评论

A General Framework and Metrics for Longitudinal Data Anonymization

A General Framework and Metrics for Longitudinal Data Anonym...

引用

international conference on privacy in statistical databases (psd)

作者： Ruiz, Nicolas Univ Rovira & Virgili CYBERCAT Ctr Cybersecur Res Catalonia Dept Comp Sci & Math UNESCO Chair Data Privacy Tarragona Spain

ISBN: (纸本)9783319997704;9783319997711

The bulk of methods in statistical disclosure control primarily deal with individual data from a cross-sectional perspective, i.e. data where individuals are observed at one single point in time. However, nowadays longitudinal data, i.e. individuals observed over multiple periods, are increasingly collected. Such data enhance undoubtedly the possibility of statistical analysis compared to cross-sectional data, but also come with some additional layers of information that have to remain practically useful in a privacy-preserving way. Building on the recently proposed permutation paradigm as an overarching approach to data anonymization, this paper establishes a general framework for the formulation of longitudinal data anonymization and proposes some universal metrics for the assessment of disclosure risk and information loss. We illustrate the application of these new tools using an empirical example.

关键词： statistical disclosure control Longitudinal data Permutation paradigm

来源：评论

学校读者我要写书评

暂无评论

The Application of Genetic Algorithms to Data Synthesis: A Comparison of Three Crossover Methods

The Application of Genetic Algorithms to Data Synthesis: A C...

引用

international conference on privacy in statistical databases (psd)

作者： Chen, Yingrui Elliot, Mark Smith, Duncan Univ Manchester Manchester M13 9PL Lancs England

ISBN: (纸本)9783319997704;9783319997711

Data synthesis is a data confidentiality method which is applied to microdata to prevent leakage of sensitive information about respondents. Instead of publishing real data, data synthesis produces an artificial dataset that does not contain the real records of respondents. This, in particular, offers significant protection against reidentification attacks. However, effective data synthesis requires retention of the key statistical properties of (and respecting the multiple utilities of) the original data. In previous work, we demonstrated the value of matrix genetic algorithms in data synthesis [4]. The current paper compares three crossover methods within a matrix GA: parallelised (two-point) crossover, matrix crossover, and parametric uniform crossover. The crossover methods are applied to three different datasets and are compared on the basis of how well they reproduce the relationships between variables in the original datasets.

关键词： Genetic algorithms Data synthesis Data privacy

来源：评论

学校读者我要写书评

暂无评论

Synthetic Data via Quantile Regression for Heavy-Tailed and Heteroskedastic Data

Synthetic Data via Quantile Regression for Heavy-Tailed and ...

引用

international conference on privacy in statistical databases (psd)

作者： Pistner, Michelle Slavkovic, Aleksandra Vilhuber, Lars Penn State Univ Dept Stat University Pk PA 16802 USA Cornell Univ Econ Dept Ithaca NY 14853 USA

ISBN: (纸本)9783319997704;9783319997711

privacy protection of confidential data is a fundamental problem faced by many government organizations and research centers. It is further complicated when data have complex structures or variables with highly skewed distributions. The statistical community addresses general privacy concerns by introducing different techniques that aim to decrease disclosure risk in released data while retaining their statistical properties. However, methods for complex data structures have received insufficient attention. We propose producing synthetic data via quantile regression to address privacy protection of heavy-tailed and heteroskedastic data. We address some shortcomings of the previously proposed use of quantile regression as a synthesis method and extend the work into cases where data have heavy tails or heteroskedastic errors. Using a simulation study and two applications, we show that there are settings where quantile regression performs as well as or better than other commonly used synthesis methods on the basis of maintaining good data utility while simultaneously decreasing disclosure risk.

关键词： Data privacy

来源：评论

学校读者我要写书评

暂无评论

Multiparty Computation with statistical Input Confidentiality via Randomized Response

Multiparty Computation with Statistical Input Confidentialit...

引用

international conference on privacy in statistical databases (psd)

作者： Domingo-Ferrer, Josep Mulero-Vellido, Rafael Soria-Comas, Jordi Univ Rovira & Virgili CYBERCAT Ctr Cybersecur Res Catalonia Dept Comp Sci & Math UNESCO Chair Data Privacy Av Paisos Catalans 26 Tarragona 43007 Catalonia Spain

ISBN: (纸本)9783319997704;9783319997711

We explore a setting in which a number of subjects want to compute on their pooled data while keeping the statistical confidentiality of their input. statistical confidentiality is different from the cryptographic confidentiality guaranteed by cryptographic multiparty secure computation: whereas in the latter nothing is disclosed about the input, in statistical input confidentiality a noise-added version of the input is disclosed, which allows more flexible computations. We propose a protocol based on local anonymization via randomized response, whereby the empirical distribution of the data of the subjects is approximated. From that distribution, most statistical calculations can be approximated as well. Regarding the accuracy of the approximation, ceteris paribus it improves with the number of subjects. Large dimensionality (that is, a large number of attributes) decreases accuracy and we propose a strategy to mitigate the dimensionality problem. We show how to characterize the privacy guarantee for each subject in terms of differential privacy. Experimental work is reported on the attained accuracy as a function of the number of respondents, number of attributes and randomized response parameters.

关键词： Multiparty anonymous computation Randomized response Local anonymization Big data privacy

来源：评论

学校读者我要写书评

暂无评论

On the privacy Guarantees of Synthetic Data: A Reassessment from the Maximum-Knowledge Attacker Perspective

On the Privacy Guarantees of Synthetic Data: A Reassessment ...

引用

international conference on privacy in statistical databases (psd)

作者： Ruiz, Nicolas Muralidhar, Krishnamurty Domingo-Ferrer, Josep Univ Rovira & Virgili CYBERCAT Ctr Cybersecur Res Catalonia Dept Comp Sci & Math UNESCO Chair Data Privacy Av Paisos Catalans 26 Tarragona 43007 Catalonia Spain Univ Oklahoma Price Coll Business Dept Mkt & Supply Chain Management 308 Brooks St Norman OK 73019 USA

ISBN: (纸本)9783319997704;9783319997711

Generating synthetic data for the dissemination of individual information in a privacy-preserving way is an approach that is often presented as superior to other statistical disclosure control techniques. The reason for such claim is straightforward at first glance: since all records disseminated are synthetic and not actual observed values, no individual can reasonably claim to face a privacy threat. Thus, and if the synthesizer used is good enough, synthetic data will potentially always offer a high level of information with low disclosure risk attached. Building on recent advances in the literature regarding the conceptualization of an intruder, this paper aims at challenging this claim by reassessing the privacy guarantees of synthetic data. Using the concept of a maximum-knowledge intruder, we demonstrate that synthetic data can in fact be always expressed as a re-arrangement of the original data and that, as a result, they may lead to configurations where disclosure risk may be higher than for nonsynthetic disclosure control approaches. We illustrate the application of these results by an empirical example.

关键词： statistical disclosure control Synthetic data Maximum-knowledge attacker

来源：评论

学校读者我要写书评

暂无评论

pMSE Mechanism: Differentially Private Synthetic Data with Maximal Distributional Similarity

<i>pMSE</i> Mechanism: Differentially Private Synthetic Data...

引用

international conference on privacy in statistical databases (psd)

作者： Snoke, Joshua Slavkovic, Aleksandra Penn State Univ Dept Stat University Pk PA 16802 USA

ISBN: (纸本)9783319997704;9783319997711

We propose a method for the release of differentially private synthetic datasets. In many contexts, data contain sensitive values which cannot be released in their original form in order to protect individuals' privacy. Synthetic data is a protection method that releases alternative values in place of the original ones, and differential privacy (DP) is a formal guarantee for quantifying the privacy loss. We propose a method that maximizes the distributional similarity of the synthetic data relative to the original data using a measure known as the pMSE, while guaranteeing epsilon-DP. We relax common DP assumptions concerning the distribution and boundedness of the original data. We prove theoretical results for the privacy guarantee and provide simulations for the empirical failure rate of the theoretical results under typical computational limitations. We give simulations for the accuracy of linear regression coefficients generated from the synthetic data compared with the accuracy of non-DP synthetic data and other DP methods. Additionally, our theoretical results extend a prior result for the sensitivity of the Gini Index to include continuous predictors.

关键词： Differential privacy Synthetic data Classification trees

来源：评论

学校读者我要写书评

暂无评论

Symmetric vs Asymmetric Protection Levels in SDC Methods for Tabular Data

Symmetric vs Asymmetric Protection Levels in SDC Methods for...

引用

international conference on privacy in statistical databases (psd)

作者： Baena, Daniel Castro, Jordi Gonzalez, Jose A. Univ Politecn Cataluna Dept Stat & Operat Res Jordi Girona 1-3 Barcelona 08034 Catalonia Spain

ISBN: (纸本)9783319997704;9783319997711

Protection levels on sensitive cells-which are key parameters of any statistical disclosure control method for tabular data-are related to the difficulty of any attacker to recompute a good estimation of the true cell values. Those protection levels are two numbers (one for the lower protection, the other for the upper protection) imposing a safety interval around the cell value, that is, no attacker should be able to recompute an estimate within such safety interval. In the symmetric case the lower and upper protection levels are equal;otherwise they are referred as asymmetric protection levels. In this work we empirically study the effect of symmetry in protection levels for three protection methods: cell suppression problem (CSP), controlled tabular adjustment (CTA), and interval protection (IP). Since CSP and CTA are mixed integer linear optimization problems, it is seen that the symmetry (or not) of protection levels affect to the CPU time needed to compute a solution. For IP, a linear optimization problem, it is observed that the symmetry heavily affects to the quality of the solution provided rather than to the solution time.

关键词： statistical disclosure control Tabular data Cell suppression Controlled tabular adjustment Interval protection Mixed integer linear optimization Linear optimization

来源：评论

学校读者我要写书评

暂无评论

Quantifying the Protection Level of a Noise Candidate for Noise Multiplication Masking Scheme

Quantifying the Protection Level of a Noise Candidate for No...

引用

international conference on privacy in statistical databases (psd)

作者： Ma, Yue Lin, Yan-Xia Krivitsky, Pavel N. Wakefield, Bradley Univ Wollongong Sch Math & Appl Stat Natl Inst Appl Stat Res Australia Wollongong NSW 2500 Australia

ISBN: (纸本)9783319997704;9783319997711

When multiplicative noises are used to perturb a set of original data, the data provider needs to ensure that the original values are not likely to be learned by data intruders from the noise-multiplied data. Different attacking strategies for unveiling the original values have been recognised in the literature, and the data provider needs to ensure that the noise-multiplied data is protected against these attacking strategies by selecting an appropriate noise generating variable. However, there are many potential attacking strategies, which makes the quantification of the protection level of a noise candidate difficult. In this paper, we argue that, to quantify the protection level a noise candidate offers to the original data against an attacking strategy, the data provider might look at the average value disclosure risk it produces. Correspondingly, we propose an optimal estimator which maximizes the average value disclosure risk. As a result, the data provider could use the maximized average value disclosure risk as a single measure for quantifying the protection level a noise candidate offers to the original data. The measure could help the data provider with the process of noise generating variable selection in practice.

关键词： Multiplicative noise masking Value disclosure risk Attacking strategy statistical disclosure control

来源：评论

学校读者我要写书评

暂无评论

没有更多数据了...

全选清除本页清除全部题录导出标记到“检索档案”

共13页 << < 3 4 5 6 7 8 9 10 11 12 > >>

检索报告对象比较合并检索0

隐藏清空

合并搜索

回到顶部

执行限定条件

内容：

评分：

请选择保存的检索档案：

请选择收藏分类：

订阅名称：

通借通还

温馨提示：

图书名称：

借书校区：

取书校区：

手机号码：

邮箱地址：

一卡通帐号：

电话和邮箱必须正确填写，我们会与您联系确认。

联系人：

所在院系：

联系邮箱：

联系电话：

内蒙古自治区呼和浩特市赛罕区大学西街235号邮编: 010021

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

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：