Privacy in statistical databases. PSD (Conference : 2004- )最新文献_第9页

Rank-Based Record Linkage for Re-Identification Risk Assessment 基于等级的再识别风险评估记录链接

Privacy in statistical databases. PSD (Conference : 2004- ) Pub Date : 2016-09-14 DOI: 10.1007/978-3-319-45381-1_17

K. Muralidhar, J. Domingo-Ferrer

引用次数: 12

Accurate Estimation of Structural Equation Models with Remote Partitioned Data 基于远程分割数据的结构方程模型的精确估计

Privacy in statistical databases. PSD (Conference : 2004- ) Pub Date : 2016-09-14 DOI: 10.1007/978-3-319-45381-1_15

Joshua Snoke, Timothy R. Brick, A. Slavkovic

引用次数: 1

Anonymization in the Time of Big Data 大数据时代的匿名化

Privacy in statistical databases. PSD (Conference : 2004- ) Pub Date : 2016-09-14 DOI: 10.1007/978-3-319-45381-1_5

J. Domingo-Ferrer, Jordi Soria-Comas

引用次数: 18

Precision Threshold and Noise: An Alternative Framework of Sensitivity Measures 精度阈值和噪声:灵敏度测量的另一种框架

Privacy in statistical databases. PSD (Conference : 2004- ) Pub Date : 2016-09-14 DOI: 10.1007/978-3-319-45381-1_2

Darren Gray

引用次数: 2

On-Average KL-Privacy and Its Equivalence to Generalization for Max-Entropy Mechanisms 最大熵机制的平均kl -隐私及其对泛化的等价性

Privacy in statistical databases. PSD (Conference : 2004- ) Pub Date : 2016-05-08 DOI: 10.1007/978-3-319-45381-1_10

Yu-Xiang Wang, Jing Lei, S. Fienberg

引用次数: 48

Propensity score based conditional group swapping for disclosure limitation of strata-defining variables. 基于倾向分数的条件分组交换对层定义变量披露限制的研究。

Privacy in statistical databases. PSD (Conference : 2004- ) Pub Date : 2016-01-01 Epub Date: 2016-08-31 DOI: 10.1007/978-3-319-45381-1_6

Anna Oganian, Goran Lesaja

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引用次数: 0

A Second Order Cone Formulation of Continuous CTA Model. 连续CTA模型的二阶锥公式。

Privacy in statistical databases. PSD (Conference : 2004- ) Pub Date : 2016-01-01 Epub Date: 2016-08-31 DOI: 10.1007/978-3-319-45381-1_4

Goran Lesaja, Jordi Castro, Anna Oganian

{"title":"A Second Order Cone Formulation of Continuous CTA Model.","authors":"Goran Lesaja, Jordi Castro, Anna Oganian","doi":"10.1007/978-3-319-45381-1_4","DOIUrl":"10.1007/978-3-319-45381-1_4","url":null,"abstract":"In this paper we consider a minimum distance Controlled Tabular Adjustment (CTA) model for statistical disclosure limitation (control) of tabular data. The goal of the CTA model is to find the closest safe table to some original tabular data set that contains sensitive information. The measure of closeness is usually measured using ℓ1 or ℓ2 norm; with each measure having its advantages and disadvantages. Recently, in [4] a regularization of the ℓ1-CTA using Pseudo-Huber function was introduced in an attempt to combine positive characteristics of both ℓ1-CTA and ℓ2-CTA. All three models can be solved using appropriate versions of Interior-Point Methods (IPM). It is known that IPM in general works better on well structured problems such as conic optimization problems, thus, reformulation of these CTA models as conic optimization problem may be advantageous. We present reformulation of Pseudo-Huber-CTA, and ℓ1-CTA as Second-Order Cone (SOC) optimization problems and test the validity of the approach on the small example of two-dimensional tabular data set.","PeriodicalId":91946,"journal":{"name":"Privacy in statistical databases. PSD (Conference : 2004- )","volume":"1 1","pages":"41-53"},"PeriodicalIF":0.0,"publicationDate":"2016-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6863437/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83269650","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Probabilistic Record Linkage for Disclosure Risk Assessment 信息披露风险评估的概率记录关联

Privacy in statistical databases. PSD (Conference : 2004- ) Pub Date : 2014-09-17 DOI: 10.1007/978-3-319-11257-2_21

N. Shlomo

引用次数: 9

Applicability of Confidentiality Methods to Personal and Business Data 保密方法对个人和商业数据的适用性

Privacy in statistical databases. PSD (Conference : 2004- ) Pub Date : 2014-09-17 DOI: 10.1007/978-3-319-11257-2_27

C. O'Keefe, N. Shlomo

引用次数: 1

Controlled Shuffling, Statistical Confidentiality and Microdata Utility: A Successful Experiment with a 10% Household Sample of the 2011 Population Census of Ireland for the IPUMS-International Database 控制洗选，统计保密性和微数据效用:2011年爱尔兰人口普查10%家庭样本的ipums -国际数据库成功实验

Privacy in statistical databases. PSD (Conference : 2004- ) Pub Date : 2014-09-17 DOI: 10.1007/978-3-319-11257-2_25

R. McCaa, K. Muralidhar, R. Sarathy, Michael Comerford, Albert Esteve-Palos

引用次数: 0