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

118 篇 会议
6 册 图书
5 篇 期刊文献

馆藏范围

129 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

105 篇 工学
- 101 篇 计算机科学与技术...
- 11 篇 软件工程
- 8 篇 信息与通信工程
- 6 篇 电气工程
- 4 篇 电子科学与技术（可...
- 2 篇 安全科学与工程
- 1 篇 光学工程
- 1 篇 动力工程及工程热...
- 1 篇 控制科学与工程
- 1 篇 建筑学
- 1 篇 土木工程
- 1 篇 测绘科学与技术
- 1 篇 生物医学工程（可授...
- 1 篇 网络空间安全
39 篇 理学
- 31 篇 统计学（可授理学、...
- 16 篇 数学
- 2 篇 系统科学
- 1 篇 物理学
5 篇 管理学
- 3 篇 图书情报与档案管...
- 2 篇 管理科学与工程(可...
- 1 篇 工商管理
4 篇 法学
- 4 篇 社会学
1 篇 医学
- 1 篇 基础医学(可授医学...
- 1 篇 临床医学
- 1 篇 药学(可授医学、理...

主题

20 篇 differential pri...
18 篇 statistical disc...
15 篇 privacy
13 篇 data privacy
13 篇 synthetic data
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 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...
2 篇 internal revenue...

作者

13 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 charest anne-sop...

语言

128 篇 英文
1 篇 中文

检索条件"任意字段=International Conference on Privacy in Statistical Databases, PSD 2022"

共 129 条记录，以下是41-50 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：

international conference on privacy in statistical databases, psd 2018

International Conference on Privacy in Statistical Databases...

引用

international conference on privacy in statistical databases, psd 2018

ISBN: (纸本)9783319997704

The proceedings contain 23 papers. The special focus in this conference is on privacy in statistical databases. The topics include: The application of genetic algorithms to data synthesis: A comparison of three crossover methods;multiparty computation with statistical input confidentiality via randomized response;Grouping of variables to facilitate SDL methods in multivariate data sets;comparative study of the effectiveness of perturbative methods for creating official microdata in Japan;a general framework and metrics for longitudinal data anonymization;reviewing the methods of estimating the density function based on masked data;protecting values close to zero under the multiplicative noise method;efficiency and sample size determination of protected data;quantifying the protection level of a noise candidate for noise multiplication masking scheme;bounded small cell adjustments for flexible frequency table generators;generalized bayesian record linkage and regression with exact error propagation;probabilistic blocking with an application to the syrian conflict;swapMob: Swapping trajectories for mobility anonymization;safely plotting continuous variables on a map;designing confidentiality on the fly methodology – three aspects;protecting census 2021 origin-destination data using a combination of cell-key perturbation and suppression;on the privacy guarantees of synthetic data: A reassessment from the maximum-knowledge attacker perspective;the quasi-multinomial synthesizer for categorical data;synthetic data via quantile regression for heavy-tailed and heteroskedastic data;some clarifications regarding fully synthetic data;differential correct attribution probability for synthetic data: An exploration.

关键词：

来源：评论

学校读者我要写书评

暂无评论

How Adversarial Assumptions Influence Re-identification Risk Measures: A COVID-19 Case Study

How Adversarial Assumptions Influence Re-identification Risk...

引用

international conference on privacy in statistical databases (psd)

作者： Zhang, Xinmeng Wan, Zhiyu Yan, Chao Brown, J. Thomas Xia, Weiyi Gkoulalas-Divanis, Aris Kantarcioglu, Murat Malin, Bradley Vanderbilt Univ Dept Comp Sci Nashville TN 37235 USA Vanderbilt Univ Med Ctr Dept Biomed Informat Nashville TN USA IBM Watson Hlth Cambridge MA USA Univ Texas Dallas Dept Comp Sci Dallas TX USA Vanderbilt Univ Med Ctr Dept Biostat Nashville TN USA

ISBN: (纸本)9783031139451;9783031139444

The COVID-19 pandemic highlights the need for broad dissemination of case surveillance data. Local and global public health agencies have initiated efforts to do so, but there remains limited data available, due in part to concerns over privacy. As a result, current COVID-19 case surveillance data sharing policies are based on strong adversarial assumptions, such as the expectation that an attacker can readily re-identify individuals based on their distinguishability in a dataset. There are various re-identification risk measures to account for adversarial capabilities;however, the current array insufficiently accounts for real world data challenges - particularly issues of missing records in resources of identifiable records that adversaries may rely upon to execute attacks (e.g., 10 50-year-old male in the de-identified dataset vs. 5 50-year-old male in the identified dataset). In this paper, we introduce several approaches to amend such risk measures and assess re-identification risk in light of how an attacker's capabilities relate to missing records. We demonstrate the potential for these measures through a record linkage attack using COVID-19 case surveillance data and voter registration records in the state of Florida. Our findings demonstrate that adversarial assumptions, as realized in a riskmeasure, can dramatically affect re-identification risk estimation. Notably, we show that the re-identification risk is likely to be substantially smaller than the typical risk thresholds, which suggests that more detailed data could be shared publicly than is currently the case.

关键词： Data sharing Re-identification risk COVID-19 Health data Data privacy

来源：评论

学校读者我要写书评

暂无评论

Private Sequential Hypothesis Testing for Statisticians: privacy, Error Rates, and Sample Size 25

Private Sequential Hypothesis Testing for Statisticians: Pri...

引用

international conference on Artificial Intelligence and Statistics

作者： Zhang, Wanrong Mei, Yajun Cummings, Rachel Harvard Univ Cambridge MA 02138 USA Georgia Inst Technol Atlanta GA 30332 USA Columbia Univ New York NY 10027 USA

The sequential hypothesis testing problem is a class of statistical analyses where the sample size is not fixed in advance. Instead, the decision-process takes in new observations sequentially to make real-time decisions for testing an alternative hypothesis against a null hypothesis until some stopping criterion is satisfied. In many common applications of sequential hypothesis testing, the data can be highly sensitive and may require privacy protection;for example, sequential hypothesis testing is used in clinical trials, where doctors sequentially collect data from patients and must determine when to stop recruiting patients and whether the treatment is effective. The field of differential privacy has been developed to offer data analysis tools with strong privacy guarantees, and has been commonly applied to machine learning and statistical tasks. In this work, we study the sequential hypothesis testing problem under a slight variant of differential privacy, known as Renyi differential privacy. We present a new private algorithm based on Wald's Sequential Probability Ratio Test (SPRT) that also gives strong theoretical privacy guarantees. We provide theoretical analysis on statistical performance measured by Type I and Type II error as well as the expected sample size. We also empirically validate our theoretical results on several synthetic databases, showing that our algorithms also perform well in practice. Unlike previous work in private hypothesis testing that focused only on the classical fixed sample setting, our results in the sequential setting allow a conclusion to be reached much earher, and thus saving the cost of collecting additional samples.

关键词： Patient treatment

来源：评论

学校读者我要写书评

暂无评论

Research on Medical Data Protection Based on Differential privacy

Research on Medical Data Protection Based on Differential Pr...

引用

2022 international conference on Cloud Computing, Internet of Things, and Computer Applications, CICA 2022

作者： Yang, Yaping Shi, Jiaqing Wang, Shiyi Shanghai Institute for Integrated Application of Network Technology Shanghai200336 China Resource and Environmental Engineering Shanghai Polytechnic University Shanghai201219 China

ISBN: (纸本)9781510656673

With the rapid development of science and technology and medical informatization, medical data has emerged on a large scale. Although medical data can provide convenient conditions for data analysis, data mining and intelligent diagnosis, the huge medical data set involves a large amount of patient private information, and there is a risk of leakage. Many existing medical institutions use databases or cloud servers to store medical data, which are also vulnerable to attacks and data leakage. The second is that third-party service providers may illegally sell the original client data. Aiming at these problems, this paper proposes a differential privacy scheme based on Laplace and summary matrix, which solves the problems of large statistical information error, low accuracy and high time and space complexity of the algorithm in the current differential privacy model. And the scheme has higher availability and is suitable for data of different scales. © 2022 SPIE.

关键词： Informatization

来源：评论

学校读者我要写书评

暂无评论

Plausible Deniability

Plausible Deniability

引用

international conference on privacy in statistical databases (psd)

作者： Sidi, David Bambauer, Jane Univ Arizona Tucson AZ 85712 USA

ISBN: (纸本)9783030575205;9783030575212

From the perspective of responsible data release, simulation is a useful tool for estimating risk from adversaries with an unknown amount of identified auxiliary information. We present a simple approach to simulation of attack on sampled datasets, along with an implementation, and demonstrate how a data steward might make use of it to evaluate the privacy risk of release for data gathered about students in the University of California system.

关键词： statistical disclosure control Simulation Education data

来源：评论

学校读者我要写书评

暂无评论

Evaluating Quality of statistical Disclosure Control Methods - VIOLAS Framework

Evaluating Quality of Statistical Disclosure Control Methods...

引用

international conference on privacy in statistical databases (psd)

作者： Dziegielewska, Olga Mil Univ Technol Gen Sylwestra Kaliskiego 2 PL-00908 Warsaw Poland

ISBN: (纸本)9783030575205;9783030575212

VIOLAS Framework (Volatile Index of Liable Accuracy for statistical Disclosure Controls) defines an assessment methodology for evaluating quality of statistical disclosure control methods by measuring the key factors determining the effectiveness of such methods. This paper describes the base model and explains how the framework may be used and extended by additional measures that may be desirable in particular IT environments or for specific data sets.

关键词： SDC quality framework VIOLAS framework Risk assessment

来源：评论

学校读者我要写书评

暂无评论

Analysis of Differentially-Private Microdata Using SIMEX

Analysis of Differentially-Private Microdata Using SIMEX

引用

international conference on privacy in statistical databases (psd)

作者： Charest, Anne-Sophie Nombo, Leila Univ Laval Quebec City PQ Canada

ISBN: (纸本)9783030575205;9783030575212

We are concerned here with the publication of microdata which preserve the confidentiality of the respondents, as measured by differential privacy. We borrow the SIMEX methodology from the measurement error literature to analyze microdata perturbed with the Laplace mechanism, hoping to recover unbiased estimates of the parameters of interest. Our simulations show that the strategy works in theory, but only for values of epsilon too large of be of used in practice. It might however be of use in the case of normal noise addition, when one does not require differential privacy.

关键词： Differential privacy Microdata SIMEX

来源：评论

学校读者我要写书评

暂无评论

Explaining Recurrent Machine Learning Models: Integral privacy Revisited

Explaining Recurrent Machine Learning Models: Integral Priva...

引用

international conference on privacy in statistical databases (psd)

作者： Torra, Vicenc Navarro-Arribas, Guillermo Galvan, Edgar Umea Univ Dept Comp Sci Umea Sweden Skovde Univ Sch Informat Skovde Sweden Univ Autonoma Barcelona Dept Informat & Commun Engn CYBERCAT Bellaterra Catalonia Spain Maynooth Univ Dept Comp Sci Naturally Inspired Computat Res Grp Maynooth Kildare Ireland

ISBN: (纸本)9783030575205;9783030575212

We have recently introduced a privacy model for statistical and machine learning models called integral privacy. A model extracted from a database or, in general, the output of a function satisfies integral privacy when the number of generators of this model is sufficiently large and diverse. In this paper we show how the maximal c-consensus meets problem can be used to study the databases that generate an integrally private solution. We also introduce a definition of integral privacy based on minimal sets in terms of this maximal c-consensus meets problem.

关键词： Integral privacy Maximal c-consensus meets Clustering Parameter selection

来源：评论

学校读者我要写书评

暂无评论

statistical Disclosure Control When Publishing on Thematic Maps

Statistical Disclosure Control When Publishing on Thematic M...

引用

international conference on privacy in statistical databases (psd)

作者： Hut, Douwe Goseling, Jasper Van Lieshout, Marie-Colette De Wolfe, Peter-Paul de Jonge, Edwin Univ Twente Enschede Netherlands Stat Netherlands The Hague Netherlands Ctr Wiskunde & Informat Amsterdam Netherlands

ISBN: (纸本)9783030575205;9783030575212

The spatial distribution of a variable, such as the energy consumption per company, is usually plotted by colouring regions of the study area according to an underlying table which is already protected from disclosing sensitive information. The result is often heavily influenced by the shape and size of the regions. In this paper, we are interested in producing a continuous plot of the variable directly from microdata and we protect it by adding random noise. We consider a simple attacker scenario and develop an appropriate sensitivity rule that can be used to determine the amount of noise needed to protect the plot from disclosing private information.

关键词： Energy utilization

来源：评论

学校读者我要写书评

暂无评论

privacy Analysis of Query-Set-Size Control

Privacy Analysis of Query-Set-Size Control

引用

international conference on privacy in statistical databases (psd)

作者： Nussbaum, Eyal Segal, Michael Ben Gurion Univ Negev Sch Elect & Comp Engn Commun Syst Engn Dept IL-84105 Beer Sheva Israel

ISBN: (纸本)9783030575205;9783030575212

Vast amounts of information of all types are collected daily about people by governments, corporations and individuals. The information is collected, for example, when users register to or use on-line applications, receive health related services, use their mobile phones, utilize search engines, or perform common daily activities. As a result, there is an enormous quantity of privately-owned records that describe individuals' finances, interests, activities, and demographics. These records often include sensitive data and may violate the privacy of the users if published. The common approach to safeguarding user information is to limit access to the data by using an authentication and authorization protocol. However, in many cases the publication of user data for statistical analysis and research can be extremely beneficial for both academic and commercial uses, such as statistical research and recommendation systems. To maintain user privacy when such a publication occurs many databases employ anonymization techniques, either on the query results or the data itself. In this paper we examine and analyze the privacy offered for aggregate queries over a data structures representing linear topologies. Additionally, we offer a privacy probability measure, indicating the probability of an attacker to obtain information defined as sensitive by utilizing legitimate queries over such a system.

关键词： privacy Datasets Anonymity Vehicular network Linear topology privacy measure

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 << < 1 2 3 4 5 6 7 8 9 10 > >>

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：