Privacy in statistical databases. PSD (Conference : 2004- )最新文献_第7页

Generalized Bayesian Record Linkage and Regression with Exact Error Propagation 精确误差传播的广义贝叶斯记录链接与回归

Privacy in statistical databases. PSD (Conference : 2004- ) Pub Date : 2018-09-26 DOI: 10.1007/978-3-319-99771-1_20

R. Steorts, A. Tancredi, B. Liseo

引用次数: 11

Reviewing the Methods of Estimating the Density Function Based on Masked Data 基于掩模数据的密度函数估计方法综述

Privacy in statistical databases. PSD (Conference : 2004- ) Pub Date : 2018-09-26 DOI: 10.1007/978-3-319-99771-1_16

Yan-Xia Lin, P. Krivitsky

引用次数: 0

Efficiency and Sample Size Determination of Protected Data 受保护数据的效率和样本量确定

Privacy in statistical databases. PSD (Conference : 2004- ) Pub Date : 2018-09-26 DOI: 10.1007/978-3-319-99771-1_18

Bradley Wakefield, Yan-Xia Lin

引用次数: 1

Multiparty Computation with Statistical Input Confidentiality via Randomized Response 基于随机响应的统计输入保密性多方计算

Privacy in statistical databases. PSD (Conference : 2004- ) Pub Date : 2018-09-26 DOI: 10.1007/978-3-319-99771-1_12

J. Domingo-Ferrer, Rafael Mulero-Vellido, Jordi Soria-Comas

引用次数: 4

Protecting Census 2021 Origin-Destination Data Using a Combination of Cell-Key Perturbation and Suppression 使用细胞键扰动和抑制相结合的方法保护2021年人口普查原点-目的地数据

Privacy in statistical databases. PSD (Conference : 2004- ) Pub Date : 2018-09-26 DOI: 10.1007/978-3-319-99771-1_4

Iain Dove, Christos Ntoumos, K. Spicer

引用次数: 1

Protecting Values Close to Zero Under the Multiplicative Noise Method 乘性噪声法下的近零保护

Privacy in statistical databases. PSD (Conference : 2004- ) Pub Date : 2018-09-26 DOI: 10.1007/978-3-319-99771-1_17

Yan-Xia Lin

引用次数: 1

pMSE Mechanism: Differentially Private Synthetic Data with Maximal Distributional Similarity pMSE机制:具有最大分布相似性的差分私有合成数据

Privacy in statistical databases. PSD (Conference : 2004- ) Pub Date : 2018-05-23 DOI: 10.1007/978-3-319-99771-1_10

Joshua Snoke, A. Slavkovic

引用次数: 43

Grouping of variables to facilitate statistical disclosure limitation methods in multivariate data sets. 对变量进行分组以方便统计披露，限制了多变量数据集的方法。

Privacy in statistical databases. PSD (Conference : 2004- ) Pub Date : 2018-01-01 DOI: 10.1007/978-3-319-99771-1_13

Anna Oganian, Ionut Iacob, Goran Lesaja

{"title":"Grouping of variables to facilitate statistical disclosure limitation methods in multivariate data sets.","authors":"Anna Oganian, Ionut Iacob, Goran Lesaja","doi":"10.1007/978-3-319-99771-1_13","DOIUrl":"10.1007/978-3-319-99771-1_13","url":null,"abstract":"Data sets that are subject to Statistical Disclosure Limitation (SDL) often have many variables of different types that need to be altered for disclosure limitation. To produce a good quality public data set, the data protector needs to account for the relationships between the variables. Hence, ideally SDL methods should not be univariate, that is, treating each variable independently of others, but multivariate, handling many variables at the same time. However, if a data set has many variables, as most government survey data do, the task of developing and implementing a multivariate approach for SDL becomes difficult. In this paper we propose a pre-masking data processing procedure which consists of clustering the variables of high dimensional data sets, so that different groups of variables can be masked independently, thus reducing the complexity of SDL. We consider different hierarchical clustering methods, including our version of hierarchical clustering algorithm, that we call K-Link, and outline how the data protector can define an appropriate number of clusters for these methods. We implemented and applied these methods to two genuine multivariate data sets. The results of the experiments show that K-Link has a potential to solve this problem efficiently. The success of the method, however, depends on the correlation structure of the data. For the data sets where most of the variables are correlated, clustering of variables and subsequent independent application of SDL methods to different clusters may lead to attenuated correlation in the masked data, even for efficient clustering methods. Thereby, the proposed approach is a trade-off between the computational complexity of multivariate SDL methods and data utility loss due to independent treatment of different clusters by SDL methods. Keywords and phrases: Statistical disclosure limitation (SDL), hierarchical clustering, dimensionality reduction.","PeriodicalId":91946,"journal":{"name":"Privacy in statistical databases. PSD (Conference : 2004- )","volume":"? ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7182379/pdf/nihms-1016601.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"37874748","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

Revisiting Interval Protection, a.k.a. Partial Cell Suppression, for Tabular Data 重新访问表格数据的间隔保护，即部分单元格抑制

Privacy in statistical databases. PSD (Conference : 2004- ) Pub Date : 2016-09-14 DOI: 10.1007/978-3-319-45381-1_1

J. Castro, Anna Via

引用次数: 2

A Rule-Based Approach to Local Anonymization for Exclusivity Handling in Statistical Databases 基于规则的统计数据库独占性处理局部匿名化方法

Privacy in statistical databases. PSD (Conference : 2004- ) Pub Date : 2016-09-14 DOI: 10.1007/978-3-319-45381-1_7

J. Albrecht, M. Fiedler, Tim Kiefer

引用次数: 0