Privacy in statistical databases. PSD (Conference : 2004- )最新文献_第3页

Privacy Analysis with a Distributed Transition System and a Data-Wise Metric 基于分布式转换系统和数据明智度量的隐私分析

Privacy in statistical databases. PSD (Conference : 2004- ) Pub Date : 2022-01-01 DOI: 10.1007/978-3-031-13945-1_2

S. Anantharaman, S. Frittella, Benjamin Nguyen

引用次数: 0

Automatic Evaluation of Disclosure Risks of Text Anonymization Methods 文本匿名化方法披露风险的自动评估

Privacy in statistical databases. PSD (Conference : 2004- ) Pub Date : 2022-01-01 DOI: 10.1007/978-3-031-13945-1_12

Benet Manzanares-Salor, David Sánchez, Pierre Lison

引用次数: 0

Explaining Recurrent Machine Learning Models: Integral Privacy Revisited 解释循环机器学习模型:重新审视积分隐私

Privacy in statistical databases. PSD (Conference : 2004- ) Pub Date : 2020-09-23 DOI: 10.1007/978-3-030-57521-2_5

V. Torra, G. Navarro-Arribas, Edgar Galván López

引用次数: 1

Private Posterior Inference Consistent with Public Information: A Case Study in Small Area Estimation from Synthetic Census Data 与公共信息一致的私人后验推断——基于综合普查数据的小区域估计案例研究

Privacy in statistical databases. PSD (Conference : 2004- ) Pub Date : 2020-09-23 DOI: 10.1007/978-3-030-57521-2_23

Jeremy Seeman, A. Slavkovic, M. Reimherr

引用次数: 11

Bayesian Modeling for Simultaneous Regression and Record Linkage 同时回归和记录链接的贝叶斯建模

Privacy in statistical databases. PSD (Conference : 2004- ) Pub Date : 2020-09-23 DOI: 10.1007/978-3-030-57521-2_15

Jiurui Tang, Jerome P. Reiter, R. Steorts

引用次数: 7

A Bayesian Nonparametric Approach to Differentially Private Data 差分私有数据的贝叶斯非参数方法

Privacy in statistical databases. PSD (Conference : 2004- ) Pub Date : 2020-09-16 DOI: 10.1007/978-3-030-57521-2_3

Fadhel Ayed, M. Battiston, G. Benedetto

引用次数: 0

Advantages of Imputation vs. Data Swapping for Statistical Disclosure Control 统计披露控制的归算与数据交换优势

Privacy in statistical databases. PSD (Conference : 2004- ) Pub Date : 2020-09-15 DOI: 10.1007/978-3-030-57521-2_20

Satkartar K. Kinney, Charlotte Looby, Feng Yu

引用次数: 0

On Different Formulations of a Continuous CTA Model. 关于连续CTA模型的不同表述。

Privacy in statistical databases. PSD (Conference : 2004- ) Pub Date : 2020-09-01 Epub Date: 2020-09-16 DOI: 10.1007/978-3-030-57521-2_12

Goran Lesaja, Ionut Iacob, Anna Oganian

{"title":"On Different Formulations of a Continuous CTA Model.","authors":"Goran Lesaja, Ionut Iacob, Anna Oganian","doi":"10.1007/978-3-030-57521-2_12","DOIUrl":"10.1007/978-3-030-57521-2_12","url":null,"abstract":"In this paper, we consider a Controlled Tabular Adjustment (CTA) model for statistical disclosure limitation of tabular data. The goal of the CTA model is to find the closest safe (masked) table to the original table that contains sensitive information. The measure of closeness is usually measured using ℓ 1 or ℓ 2 norm. However, in the norm-based CTA model, there is no control of how well the statistical properties of the data in the original table are preserved in the masked table. Hence, we propose a different criterion of \"closeness\" between the masked and original table which attempts to minimally change certain statistics used in the analysis of the table. The Chi-square statistic is among the most utilized measures for the analysis of data in two-dimensional tables. Hence, we propose a Chi-square CTA model which minimizes the objective function that depends on the difference of the Chi-square statistics of the original and masked table. The model is non-linear and non-convex and therefore harder to solve which prompted us to also consider a modification of this model which can be transformed into a linear programming model that can be solved more efficiently. We present numerical results for the two-dimensional table illustrating our novel approach and providing a comparison with norm-based CTA models.","PeriodicalId":91946,"journal":{"name":"Privacy in statistical databases. PSD (Conference : 2004- )","volume":"12276 ","pages":"166-179"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8057307/pdf/nihms-1676971.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38822223","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

Multivariate Top-Coding for Statistical Disclosure Limitation. 统计披露限制的多变量顶部编码。

Privacy in statistical databases. PSD (Conference : 2004- ) Pub Date : 2020-09-01 Epub Date: 2020-09-16 DOI: 10.1007/978-3-030-57521-2_10

Anna Oganian, Ionut Iacob, Goran Lesaja

{"title":"Multivariate Top-Coding for Statistical Disclosure Limitation.","authors":"Anna Oganian, Ionut Iacob, Goran Lesaja","doi":"10.1007/978-3-030-57521-2_10","DOIUrl":"10.1007/978-3-030-57521-2_10","url":null,"abstract":"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.","PeriodicalId":91946,"journal":{"name":"Privacy in statistical databases. PSD (Conference : 2004- )","volume":"12276 ","pages":"136-148"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8057308/pdf/nihms-1676966.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38822222","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

Statistical Disclosure Control When Publishing on Thematic Maps 专题地图出版时的统计资料披露管制

Privacy in statistical databases. PSD (Conference : 2004- ) Pub Date : 2020-07-14 DOI: 10.1007/978-3-030-57521-2_14

Douwe Hut, J. Goseling, M. V. Lieshout, P. D. Wolf, E. D. Jonge

引用次数: 1