Pub Date : 2022-07-02DOI: 10.48550/arXiv.2207.03339
C. Little, M. Elliot, R. Allmendinger
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.
{"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}
Pub Date : 2022-05-12DOI: 10.1007/978-3-031-13945-1_15
James Jackson, R. Mitra, Brian Francis, Iain Dove
{"title":"On Integrating the Number of Synthetic Data Sets m into the a priori Synthesis Approach","authors":"James Jackson, R. Mitra, Brian Francis, Iain Dove","doi":"10.1007/978-3-031-13945-1_15","DOIUrl":"https://doi.org/10.1007/978-3-031-13945-1_15","url":null,"abstract":"","PeriodicalId":91946,"journal":{"name":"Privacy in statistical databases. PSD (Conference : 2004- )","volume":"99 1","pages":"205-219"},"PeriodicalIF":0.0,"publicationDate":"2022-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73213257","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}
Pub Date : 2022-05-08DOI: 10.48550/arXiv.2205.03939
K. Muralidhar
: 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.
{"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}
Pub Date : 2022-02-10DOI: 10.1007/978-3-031-13945-1_21
Paul L. Francis
{"title":"A Note on the Misinterpretation of the US Census Re-identification Attack","authors":"Paul L. Francis","doi":"10.1007/978-3-031-13945-1_21","DOIUrl":"https://doi.org/10.1007/978-3-031-13945-1_21","url":null,"abstract":"","PeriodicalId":91946,"journal":{"name":"Privacy in statistical databases. PSD (Conference : 2004- )","volume":"14 1","pages":"299-311"},"PeriodicalIF":0.0,"publicationDate":"2022-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89668329","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}
Pub Date : 2022-01-01DOI: 10.1007/978-3-031-13945-1_6
F. Geyer, R. Tent, Michel Reiffert, Sarah Giessing
{"title":"Perspectives for Tabular Data Protection - How About Synthetic Data?","authors":"F. Geyer, R. Tent, Michel Reiffert, Sarah Giessing","doi":"10.1007/978-3-031-13945-1_6","DOIUrl":"https://doi.org/10.1007/978-3-031-13945-1_6","url":null,"abstract":"","PeriodicalId":91946,"journal":{"name":"Privacy in statistical databases. PSD (Conference : 2004- )","volume":"47 1","pages":"77-91"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81286721","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}
Pub Date : 2022-01-01DOI: 10.1007/978-3-031-13945-1_10
J. Domingo-Ferrer
{"title":"Tit-for-Tat Disclosure of a Binding Sequence of User Analyses in Safe Data Access Centers","authors":"J. Domingo-Ferrer","doi":"10.1007/978-3-031-13945-1_10","DOIUrl":"https://doi.org/10.1007/978-3-031-13945-1_10","url":null,"abstract":"","PeriodicalId":91946,"journal":{"name":"Privacy in statistical databases. PSD (Conference : 2004- )","volume":"477 1","pages":"133-141"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86752652","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}
Pub Date : 2022-01-01DOI: 10.1007/978-3-031-13945-1
{"title":"Privacy in Statistical Databases: International Conference, PSD 2022, Paris, France, September 21–23, 2022, Proceedings","authors":"","doi":"10.1007/978-3-031-13945-1","DOIUrl":"https://doi.org/10.1007/978-3-031-13945-1","url":null,"abstract":"","PeriodicalId":91946,"journal":{"name":"Privacy in statistical databases. PSD (Conference : 2004- )","volume":"29 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87929303","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}
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