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

121 篇 会议
6 册 图书
5 篇 期刊文献

馆藏范围

132 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...
- 11 篇 软件工程
- 7 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 网络空间安全
- 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 安全科学与工程
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
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
3 篇 法学
- 3 篇 社会学

主题

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

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 minnesota popula...
2 篇 rovira i virgili...
2 篇 rti int res tria...
2 篇 univ lancaster l...
2 篇 karlsruhe inst t...

作者

13 篇 domingo-ferrer j...
6 篇 muralidhar krish...
5 篇 oganian anna
5 篇 josep domingo-fe...
4 篇 ruggles steven
4 篇 castro jordi
4 篇 sanchez david
4 篇 vilhuber lars
3 篇 mccaa robert
3 篇 slavkovic aleksa...
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 onen melek
2 篇 zari oualid
2 篇 charest anne-sop...

语言

131 篇 英文
1 篇 中文

检索条件"任意字段=International Conference on Privacy in Statistical Databases, PSD 2010"

共 132 条记录，以下是21-30 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：

privacy and Disclosure Risks in Spatial Dynamic Microsimulations 1

引用

international conference on privacy in statistical databases (psd)

作者： Brenzel, Hanna Palm, Martin Weymeirsch, Jan Muennich, Ralf Fed Stat Off Destatis Gustav Stresemann Ring 11 D-65189 Wiesbaden Germany Trier Univ Univ Ring 15 D-54296 Trier Germany

ISBN: (数字)9783031696510

ISBN: (纸本)9783031696503;9783031696510

Microsimulations are used as a tool for evidence-based policy, to better understand the impact of policies on society. However, the quality of the outcome considerably depends on the quality of the data in use. In order to provide a rich set of variables on granular details, synthetic data may be involved. This becomes even more important in dynamic microsimulation where projection into the future is simulated or when providing an open data environment for the research community with a vast amount of variables including geocoded information. The present paper discusses opportunities and challenges of such synthetic but realistic data generation in a microsimulation data lab, which includes two steps of disclosure control. First, the base information which is used for the synthetic data generation has to be reviewed in terms of disclosure risks. Second, the data generating process must be ensured to not systematically reproduce rare events for (synthetic) individuals or replicating original input data. Otherwise, due to the large amount of additional information, this may lead to a cumulative effect of the individual re-identification risks. In order to make these output data of dynamic spatial microsimulations available in the sense of open and reproducible research, such statistical disclosure risks must be excluded a priori. This study examines whether disclosure risks may occur when synthetic data is generated via anonymized data from official statistics sources and how these can be avoided in principle. Furthermore, we discuss methods within the framework of spatial dynamic microsimulation frameworks that automatically ensure the standards of statistical disclosure control as well as official statistics data providers during simulation runs.

关键词： statistical Disclosure Control Dynamic Microsimulation Synthetic Population Small Area Spatial Simualtion Monte-Carlo Simulation Confidentiality Open Reproducible Research

来源：评论

学校读者我要写书评

暂无评论

DISCOLEAF: Personalized DIScretization of COntinuous Attributes for LEArning with Federated Decision Trees 1

引用

international conference on privacy in statistical databases (psd)

作者： Kwatra, Saloni Torra, Vicenc Umea Univ Dept Comp Sci Umea Sweden

ISBN: (数字)9783031696510

ISBN: (纸本)9783031696503;9783031696510

Federated learning is a distributed machine learning framework, in which each client participating to the federation trains a machine learning model on its data, and shares the trained model information with a central server, which aggregates, and sends the aggregated information back to the distributed clients. The machine learning model we choose to work with is decision trees, due to their simplicity, and interpretability. On that note, we propose a full-fledged federated pipeline, which includes discretization and learning with decision trees for the horizontally partitioned data. Our federated discretization approach can be plugged-in as a prepossessing step before any other federated learning algorithm. During discretization, we ensure that each client creates the number of discrete bins, according to their own data/choice. Hence, our approach is both federated and personalized. After discretization, we propose to apply the post randomization method to protect the discretized data with differential privacy guarantees. After protecting its database, each client trains a decision tree classifier on its protected database locally, and shares the nodes, containing the split attribute, and the split value with the central server. The central server obtains the most occurred split attribute, and combines the split values. This process goes on until all the nodes to be merged are leaf nodes. The central server then shares the merged tree with the distributed clients. Hence, our proposed framework performs personalized, privacy-preserving federated learning with decision trees by discretizing continuous attributes, and then masking them prior to the training stage. We call our proposed framework DISCOLEAF.

关键词： Federated Learning Discretization Decision Trees Differential privacy Decision Tree Aggregation

来源：评论

学校读者我要写书评

暂无评论

Multivariate Mean Comparison Under Differential privacy

Multivariate Mean Comparison Under Differential Privacy

引用

international conference on privacy in statistical databases (psd)

作者： Dunsche, Martin Kutta, Tim Dette, Holger Ruhr Univ Bochum Germany

ISBN: (纸本)9783031139451;9783031139444

The comparison of multivariate population means is a central task of statistical inference. While statistical theory provides a variety of analysis tools, they usually do not protect individuals' privacy. This knowledge can create incentives for participants in a study to conceal their true data (especially for outliers), which might result in a distorted analysis. In this paper, we address this problem by developing a hypothesis test for multivariate mean comparisons that guarantees differential privacy to users. The test statistic is based on the popular Hotelling's t(2)-statistic, which has a natural interpretation in terms of the Mahalanobis distance. In order to control the type-1-error, we present a bootstrap algorithm under differential privacy that provably yields a reliable test decision. In an empirical study, we demonstrate the applicability of this approach.

关键词： Differential privacy Private testing Private bootstrap

来源：评论

学校读者我要写书评

暂无评论

Towards Integrally Private Clustering: Overlapping Clusters for High privacy Guarantees

Towards Integrally Private Clustering: Overlapping Clusters ...

引用

international conference on privacy in statistical databases (psd)

作者： Torra, Vicenc Umea Univ Dept Comp Sci Umea Sweden

ISBN: (纸本)9783031139451;9783031139444

privacy for re-identification, k-anonymity, and differential privacy are the main privacy models considered in the literature on data privacy. We introduced an alternative privacy model called integral privacy, that can be seen as a model for computations avoiding membership inference attacks, as well as other inferences, on aggregates and computations from data (e.g., machine learning models and statistics). In previous papers we have shown how we can compute integrally private statistics (e.g., means and variance), decision trees, and regression. In this paper we introduce clustering with overlapping clusters. The goal is to produce integrally private clusters. We formulate the problem in terms of an optimization problem, and provide a (sub-optimal) solution based on genetic algorithms.

关键词： Data privacy statistical disclosure control Clustering Integral privacy Overlapping clustering

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 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

Challenges in Measuring Utility for Fully Synthetic Data

Challenges in Measuring Utility for Fully Synthetic Data

引用

international conference on privacy in statistical databases (psd)

作者： Drechsler, Joerg Inst Employment Res Regensburger Str 104 D-90478 Nurnberg Germany

ISBN: (纸本)9783031139451;9783031139444

Evaluating the utility of the generated data is a pivotal step in any synthetic data project. Most projects start by exploring various synthesis approaches trying to identify the most suitable synthesis strategy for the data at hand. Utility evaluations are also always necessary to decide whether the data are of sufficient quality to be released. Various utility measures have been proposed for this purpose in the literature. However, as I will show in this paper, some of these measures can be misleading when considered in isolation while others seem to be inappropriate to assess whether the synthetic data are suitable to be released. This illustrates that a detailed validity assessment looking at various dimensions of utility will always be inevitable to find the optimal synthesis strategy.

关键词： Confidence interval overlap Confidentiality Global utility pMSE privacy

来源：评论

学校读者我要写书评

暂无评论

On privacy of Multidimensional Data Against Aggregate Knowledge Attacks

On Privacy of Multidimensional Data Against Aggregate Knowle...

引用

international conference on privacy in statistical databases (psd)

作者： Laouir, Ala Eddine Imine, Abdessamad Lorraine Univ CNRS INRIA F-54506 Vandoeuvre Les Nancy France

ISBN: (纸本)9783031139451;9783031139444

In this paper, we explore the privacy problem of individuals in publishing data cubes using SUM queries, where a malicious user is expected to have an aggregate knowledge (e.g., average information) over the data ranges. We propose an efficient solution that maximizes the utility of SUM queries while mitigating inference attacks from aggregate knowledge. Our solution combines cube compression (i.e., suppression of data cells) and data perturbation. First, we give a formal statement for the privacy of aggregate knowledge based on data suppression. Next, we develop a Linear Programming (LP) model to determine the number of data cells to be removed and a heuristic method to effectively suppress data cells. To overcome the limitation of data suppression, we complement it with suitable data perturbation. Through empirical evaluation on benchmark data cubes, we show that our solution gives best performance in terms of utility and privacy.

关键词： Data cubes privacy preservation Cell suppression Cell perturbation Cube compression

来源：评论

学校读者我要写书评

暂无评论

privacy in Practice: Latest Achievements of the Eustat SDC Group

Privacy in Practice: Latest Achievements of the Eustat SDC G...

引用

international conference on privacy in statistical databases (psd)

作者： Miranda, Ana Mas, Marta Ayestaran, Marina Basque Stat Off Vitoria Spain

ISBN: (纸本)9783031139451;9783031139444

Maintaining the privacy of the data providers, preserving the confidentiality of the information they provide and its use only for statistical purposes must be fully guaranteed within the statistical activity. This principle largely underpins the credibility of a statistical organization and must be present in all phases of statistical production [2]. Since 1998, research work has been carried out in this field at Eustat: the provision of a scholarship for the study of the most common techniques [3] for protecting microdata files and statistical tables, the application of specific protection measures to real data, the establishment of standard criteria for the protection of statistical information and the widespread dissemination of microdata. In 2018, an expert group was created at Eustat that coordinates and promotes all these tasks within the Organisation. This paper collects the main works and results obtained by the group over these latest five years. Firstly, the preparation and updating of the criteria document on confidentiality and data protection in statistical dissemination is described. The aim is to provide the staff with a guide to basic confidentiality criteria when preparing statistical products for dissemination. Next, the type of analysis that is carried out to prepare secure microdata for dissemination (public use files) is shown. As an example, the analysis carried out for the microdata of the Population Survey in Relation to Activity (PRA) is included. Finally, a solution for the automatic protection of statistical tables by t-Argus [5] using a SAS macro is presented. Specifically, the application to tables of the Directory of Economic Activities of Eustat is shown.

关键词： statistical disclosure control Official statistics Microdata protection statistical tables protection

来源：评论

学校读者我要写书评

暂无评论

Generation of Synthetic Trajectory Microdata from Language Models

Generation of Synthetic Trajectory Microdata from Language M...

引用

international conference on privacy in statistical databases (psd)

作者： Blanco-Justicia, Alberto Jebreel, Najeeb Moharram Manjon, Jesus A. Domingo-Ferrer, Josep Univ Rovira & Virgili Dept Comp Engn & Math Av Paisos Catalans 26 Tarragona 43007 Catalonia Spain

ISBN: (纸本)9783031139451;9783031139444

Releasing and sharing mobility data, and specifically trajectories, is necessary for many applications, from infrastructure planning to epidemiology. Yet, trajectories are highly sensitive data, because the points visited by an individual can be identifying and also confidential. Hence, trajectories must be anonymized before releasing or sharing them. While most contributions to the trajectory anonymization literature take statistical approaches, deep learning is increasingly being used. We observe that natural language sentences and trajectories share a sequential nature that can be exploited in similar ways. In this paper, we present preliminary work on generating synthetic trajectories using machine learning models typically used for natural language processing. Our empirical results attest to the quality of the generated synthetic trajectories. Furthermore, our methods allow discovering natural neighborhoods based on trajectories.

关键词： privacy Synthetic data generation Mobility data

来源：评论

学校读者我要写书评

暂无评论

Secure and Non-interactive k-NN Classifier Using Symmetric Fully Homomorphic Encryption

Secure and Non-interactive k-NN Classifier Using Symmetric F...

引用

international conference on privacy in statistical databases (psd)

作者： Ameur, Yulliwas Aziz, Rezak Audigier, Vincent Bouzefrane, Samia Cnam CEDRIC Lab 292 Rue St Martin F-75141 Paris France

ISBN: (纸本)9783031139451;9783031139444

"Machine learning as a service" (MLaaS) in the cloud accelerates the adoption of machine learning techniques. Nevertheless, the externalization of data on the cloud raises a serious vulnerability issue because it requires disclosing private data to the cloud provider. This paper deals with this problem and brings a solution for the K-nearest neighbors (k-NN) algorithm with a homomorphic encryption scheme (called TFHE) by operating on end-to-end encrypted data while preserving privacy. The proposed solution addresses all stages of k-NN algorithm with fully encrypted data, including the majority vote for the class-label assignment. Unlike existing techniques, our solution does not require intermediate interactions between the server and the client when executing the classification task. Our algorithm has been assessed with quantitative variables and has demonstrated its efficiency on large and relevant real-world data sets while scaling well across different parameters on simulated data.

关键词： k-nearest neighbors Homomorphic encryption TFHE Data privacy IoT Cloud computing privacy-preserving

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14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：