Privacy in statistical databases. PSD (Conference : 2004- )最新文献

Comparing the Utility and Disclosure Risk of Synthetic Data with Samples of Microdata 综合数据与微数据样本的效用与披露风险比较

Privacy in statistical databases. PSD (Conference : 2004- ) Pub Date : 2022-07-02 DOI: 10.48550/arXiv.2207.03339

C. Little, M. Elliot, R. Allmendinger

{"title":"Comparing the Utility and Disclosure Risk of Synthetic Data with Samples of Microdata","authors":"C. Little, M. Elliot, R. Allmendinger","doi":"10.48550/arXiv.2207.03339","DOIUrl":"https://doi.org/10.48550/arXiv.2207.03339","url":null,"abstract":"Most statistical agencies release randomly selected samples of Census microdata, usually with sample fractions under 10% and with other forms of statistical disclosure control (SDC) applied. An alternative to SDC is data synthesis, which has been attracting growing interest, yet there is no clear consensus on how to measure the associated utility and disclosure risk of the data. The ability to produce synthetic Census microdata, where the utility and associated risks are clearly understood, could mean that more timely and wider-ranging access to microdata would be possible. This paper follows on from previous work by the authors which mapped synthetic Census data on a risk-utility (R-U) map. The paper presents a framework to measure the utility and disclosure risk of synthetic data by comparing it to samples of the original data of varying sample fractions, thereby identifying the sample fraction which has equivalent utility and risk to the synthetic data. Three commonly used data synthesis packages are compared with some interesting results. Further work is needed in several directions but the methodology looks very promising.","PeriodicalId":91946,"journal":{"name":"Privacy in statistical databases. PSD (Conference : 2004- )","volume":"117 1","pages":"234-249"},"PeriodicalIF":0.0,"publicationDate":"2022-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75755999","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

Utility and Disclosure Risk for Differentially Private Synthetic Categorical Data 差异私有合成分类数据的效用与披露风险

Privacy in statistical databases. PSD (Conference : 2004- ) Pub Date : 2022-06-03 DOI: 10.1007/978-3-031-13945-1_18

G. Raab

引用次数: 1

On Integrating the Number of Synthetic Data Sets m into the a priori Synthesis Approach 先验综合方法中综合数据集个数m的研究

Privacy in statistical databases. PSD (Conference : 2004- ) Pub Date : 2022-05-12 DOI: 10.1007/978-3-031-13945-1_15

James Jackson, R. Mitra, Brian Francis, Iain Dove

引用次数: 2

A Re-examination of the Census Bureau Reconstruction and Reidentification Attack 重新审视人口普查局重建和重新识别攻击

Privacy in statistical databases. PSD (Conference : 2004- ) Pub Date : 2022-05-08 DOI: 10.48550/arXiv.2205.03939

K. Muralidhar

{"title":"A Re-examination of the Census Bureau Reconstruction and Reidentification Attack","authors":"K. Muralidhar","doi":"10.48550/arXiv.2205.03939","DOIUrl":"https://doi.org/10.48550/arXiv.2205.03939","url":null,"abstract":": Recent analysis by researchers at the U.S. Census Bureau claims that by reconstructing the tabular data released from the 2010 Census, it is possible to reconstruct the original data and, using an accurate external data file with identity, reidentify 179 million respondents (approximately 58% of the population). This study shows that there are a practically infinite number of possible reconstructions, and each reconstruction leads to assigning a different identity to the respondents in the reconstructed data. The results reported by the Census Bureau researchers are based on just one of these infinite possible reconstructions and is easily refuted by an alternate reconstruction. Without definitive proof that the reconstruction is unique, or at the very least, that most reconstructions lead to the assignment of the same identity to the same respondent, claims of confirmed reidentification are highly suspect and easily refuted. The Census releases data at different geographic levels: nation, state, county, tract, block group, and block. The final three are census-defined constructs and do not necessarily correspond to traditional geographic classification. For personal level data, the data at the smaller geographic level is aggregated to the next higher level, that is, the results at the block level are aggregated to block groups, block groups are aggregated to tracts, etc. The multiple tables that are released (Total Population, Sex by Age, Total Races, and others) are all aggregations of the most detailed data release (Age by Sex, by Race, by Ethnicity). The different tables released form the basis of the reconstruction of the respondent microdata.","PeriodicalId":91946,"journal":{"name":"Privacy in statistical databases. PSD (Conference : 2004- )","volume":"39 1","pages":"312-323"},"PeriodicalIF":0.0,"publicationDate":"2022-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82611151","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 7

A Note on the Misinterpretation of the US Census Re-identification Attack 关于对美国人口普查重新识别攻击的误解的说明

Privacy in statistical databases. PSD (Conference : 2004- ) Pub Date : 2022-02-10 DOI: 10.1007/978-3-031-13945-1_21

Paul L. Francis

引用次数: 3

Perspectives for Tabular Data Protection - How About Synthetic Data? 表格数据保护的视角——合成数据如何?

Privacy in statistical databases. PSD (Conference : 2004- ) Pub Date : 2022-01-01 DOI: 10.1007/978-3-031-13945-1_6

F. Geyer, R. Tent, Michel Reiffert, Sarah Giessing

引用次数: 1

Tit-for-Tat Disclosure of a Binding Sequence of User Analyses in Safe Data Access Centers 安全数据访问中心中用户分析绑定序列的针锋相对的披露

Privacy in statistical databases. PSD (Conference : 2004- ) Pub Date : 2022-01-01 DOI: 10.1007/978-3-031-13945-1_10

J. Domingo-Ferrer

引用次数: 0

Membership Inference Attack Against Principal Component Analysis 针对主成分分析的隶属推理攻击

Privacy in statistical databases. PSD (Conference : 2004- ) Pub Date : 2022-01-01 DOI: 10.1007/978-3-031-13945-1_19

Oualid Zari, Javier Parra-Arnau, Ayşe Ünsal, T. Strufe, Melek Önen

引用次数: 1

On Privacy of Multidimensional Data Against Aggregate Knowledge Attacks 针对聚合知识攻击的多维数据隐私研究

Privacy in statistical databases. PSD (Conference : 2004- ) Pub Date : 2022-01-01 DOI: 10.1007/978-3-031-13945-1_7

Ala Eddine Laouir, Abdessamad Imine

引用次数: 0

Privacy in Statistical Databases: International Conference, PSD 2022, Paris, France, September 21–23, 2022, Proceedings 统计数据库中的隐私:国际会议，PSD 2022，巴黎，法国，2022年9月21-23日，论文集

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

引用次数: 0