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检索条件"任意字段=Conference on Privacy in Statistical Databases 2010"

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International conference on privacy in statistical databases, PSD 2020

International Conference on Privacy in Statistical Databases...

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International conference on privacy in statistical databases, PSD 2020

ISBN: (纸本)9783030575205

The proceedings contain 25 papers. The special focus in this conference is on privacy in statistical databases. The topics include: Calculation of Risk Probabilities for the Cell Key Method;On Different Formulations of a Continuous CTA Model;privacy Analysis of Query-Set-Size Control;statistical Disclosure Control When Publishing on Thematic Maps;bayesian Modeling for Simultaneous Regression and Record Linkage;probabilistic Blocking and Distributed Bayesian Entity Resolution;secure Matrix Computation: A Viable Alternative to Record Linkage?;a Synthetic Supplemental Public Use File of Low-Income Information Return Data: Methodology, Utility, and privacy Implications;integrating Differential privacy in the statistical Disclosure Control Tool-Kit for Synthetic Data Production;ϵ -Differential privacy for Microdata Releases Does Not Guarantee Confidentiality (Let Alone Utility);advantages of Imputation vs. Data Swapping for statistical Disclosure Control;Evaluating Quality of statistical Disclosure Control Methods – VIOLAS Framework;detecting Bad Answers in Survey Data Through Unsupervised Machine Learning;private Posterior Inference Consistent with Public Information: A Case Study inSmall Area Estimation from Synthetic Census Data;differential privacy and Its Applicability for Official Statistics in Japan – A Comparative Study Using Small Area Data from the Japanese Population Census;disclosure Avoidance in the Census Bureaus 2010 Demonstration Data Product;a Bayesian Nonparametric Approach to Differentially Private Data;a Partitioned Recoding Scheme for privacy Preserving Data Publishing;explaining Recurrent Machine Learning Models: Integral privacy Revisited;utility-Enhancing Flexible Mechanisms for Differential privacy;plausible Deniability;Analysis of Differentially-Private Microdata Using SIMEX;an Analysis of Different Notions ofEffectiveness in k-Anonymity.

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Escalation of Commitment: A Case Study of the United States Census Bureau Efforts to Implement Differential privacy for the 2020 Decennial Census 1

引用

International conference on privacy in statistical databases (PSD)

作者： Muralidhar, Krishnamurty Ruggles, Steven Univ Oklahoma Norman OK 73019 USA Univ Minnesota Minneapolis MN 55455 USA IPUMS Minneapolis MN 55455 USA

ISBN: (数字)9783031696510

ISBN: (纸本)9783031696503;9783031696510

In 2017, the United States Census Bureau announced that because of high disclosure risk in the methodology (data swapping) used to produce tabular data for the 2010 census, a different protection mechanism based on differential privacy would be used for the 2020 census. While there have been many studies evaluating the result of this change, there has been no rigorous examination of disclosure risk claims resulting from the released 2010 tabular data. In this study we perform such an evaluation. We show that the procedures used to evaluate disclosure risk are unreliable and resulted in inflated disclosure risk. Demonstration data products released using the new procedure were also shown to have poor utility. However, since the Census Bureau had already committed to a different procedure, they had no option except to escalate their commitment. The result of such escalation is that the 2020 tabular data release offers neither privacy nor accuracy.

关键词： Census disclosure privacy

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Disclosure Avoidance in the Census Bureau's 2010 Demonstration Data Product

Disclosure Avoidance in the Census Bureau's 2010 Demonstrati...

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International conference on privacy in statistical databases (PSD)

作者： Van Riper, David Kugler, Tracy Ruggles, Steven Univ Minnesota Minnesota Populat Ctr Minneapolis MN 55455 USA

ISBN: (纸本)9783030575205;9783030575212

Producing accurate, usable data while protecting respondent privacy are dual mandates of the US Census Bureau. In 2019, the Census Bureau announced it would use a new disclosure avoidance technique, based on differential privacy, for the 2020 Decennial Census of Population and Housing [19]. Instead of suppressing data or swapping sensitive records, differentially private methods inject noise into counts to protect privacy. Unfortunately, noise injection may also make the data less useful and accurate. This paper describes the differentially private Disclosure Avoidance System (DAS) used to prepare the 2010 Demonstration Data Product (DDP). It describes the policy decisions that underlie the DAS and how the DAS uses those policy decisions to produce differentially private data. Finally, it discusses usability and accuracy issues in the DDP, with a focus on occupied housing unit counts. Occupied housing unit counts in the DDP differed greatly from 2010 Summary File 1 differed greatly, and the paper explains possible sources of the differences.

关键词： Differential privacy 2020 US Decennial Census Accuracy

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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

Controlled Shuffling, Statistical Confidentiality and Microd...

引用

UNESCO Chair in Data privacy International conference on privacy in statistical databases (PSD)

作者： McCaa, Robert Muralidhar, Krishnamurty Sarathy, Rathindra Comerford, Michael Esteve-Palos, Albert Minnesota Populat Ctr Minneapolis MN 55455 USA

ISBN: (纸本)9783319112572;9783319112565

IPUMS-International disseminates more than two hundred-fifty integrated, confidentialized census microdata samples to thousands of researchers world-wide at no cost. The number of samples is increasing at the rate of several dozen per year, as quickly as the task of integrating metadata and microdata is completed. Protecting the statistical confidentiality and privacy of individuals represented in the microdata is a sine qua non of the IPUMS project. For the 2010 round of censuses, even greater protections are required, while researchers are demanding ever higher precision and utility. This paper describes a tripartite collaborative experiment using a ten percent household sample of the 2011 census of Ireland to estimate risk, mask the microdata using controlled shuffling, and assess analytical utility by comparing the masked data against the unprotected source microdata. Controlled shuffling exploits hierarchically ordered coding schemes to protect privacy and enhance utility. With controlled shuffling, the lesson seems to be the more detail means less risk and greater utility. Overall, despite substantial perturbation of the masked dataset (30% of adults on one or more characteristic), we find that data utility is very high and information loss is slight, even for fairly complex analytical problems.

关键词： controlled shuffling population census microdata sample data privacy data utility statistical disclosure controls IPUMS-International Ireland

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privacy in statistical databases - UNESCO Chair in Data privacy International conference, PSD 2010, Proceedings

Privacy in Statistical Databases - UNESCO Chair in Data Priv...

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International conference on privacy in statistical databases, PSD 2010

ISBN: (纸本)3642158374

The proceedings contain 25 papers. The topics discussed include: privacy disclosure analysis and control for 2D contingency tables containing inaccurate data;a tool for analyzing and fixing infeasible RCTA instances;branch-and-cut versus cut-and-branch algorithms for cell suppression;data swapping for protecting census tables;eliminating small cells from census counts tables: some considerations on transition probabilities;uncertainty for anonymity and 2-dimensional range query distortion;pram optimization using an evolutionary algorithm;multiplicative noise protocols;measurement error and statistical disclosure control;semantic microaggregation for the anonymization of query logs;data environment analysis and the key variable mapping system;using support vector machines for generating synthetic datasets;synthetic data for small area estimation;and differential privacy and the risk-utility tradeoff for multi-dimensional contingency tables.

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Some Additional Insights on Applying Differential privacy for Numeric Data

Some Additional Insights on Applying Differential Privacy fo...

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conference on privacy in statistical databases 2010

作者： Sarathy, Rathindra Muralidhar, Krish Oklahoma State Univ Stillwater OK 74078 USA Univ Kentucky Lexington KY 40506 USA

ISBN: (纸本)9783642158377

Recently Sarathy and Muralidhar (2009) provided the first attempt at illustrating the implementation of differential privacy for numerical data. In this paper, we attempt to provide further insights on the results that are observed when Laplace based noise addition is used to protect numerical data in order to satisfy differential privacy. Our results raise serious concerns regarding the viability of differential privacy and Laplace noise addition as appropriate procedures for protecting numerical data.

关键词： Differential privacy Laplace noise addition Numerical data

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Does Differential privacy Protect Terry Gross' privacy?

Does Differential Privacy Protect Terry Gross' Privacy?

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conference on privacy in statistical databases 2010

作者： Muralidhar, Krish Sarathy, Rathindra Univ Kentucky Lexington KY 40506 USA Oklahoma State Univ Stillwater OK 74078 USA

ISBN: (纸本)9783642158377

The concept of differential privacy was motivated through the example of Terry Gross' height in Dwork (2006). In this paper, we show that when a procedure based on differential privacy is implemented, it neither protects Terry Gross' privacy nor does it provide meaningful responses to queries. We also provide an additional illustration using income data from the US Census. These illustrations raise serious questions regarding the efficacy of using differential privacy based masking mechanism for numerical data.

关键词： Differential privacy Laplace noise addition Numerical data

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Differential privacy and the Risk-Utility Tradeoff for Multi-dimensional Contingency Tables

Differential Privacy and the Risk-Utility Tradeoff for Multi...

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conference on privacy in statistical databases 2010

作者： Fienberg, Stephen E. Rinaldo, Alessandro Yang, Xiaolin Carnegie Mellon Univ Dept Stat Pittsburgh PA 15213 USA

ISBN: (纸本)9783642158377

The methodology of differential privacy has provided a strong definition of privacy which in some settings, using a mechanism of doubly-exponential noise addition, also allows for extraction of informative statistics from databases. A recent paper extends this approach to the release of a specified set of margins from a multi-way contingency table. privacy protection in such settings implicitly focuses on small cell counts that might allow for the identification of units that are unique in the database. We explore how well the mechanism works in the context of a series of examples, and the extent to which the proposed differential-privacy mechanism allows for sensible inferences from the released data.

关键词： Artificial intelligence

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privacy-Preserving Record Linkage

Privacy-Preserving Record Linkage

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conference on privacy in statistical databases 2010

作者： Hall, Rob Fienberg, Stephen E. Carnegie Mellon Univ Dept Stat Machine Learning Dept Pittsburgh PA 15213 USA Carnegie Mellon Univ Cylab Pittsburgh PA 15213 USA

ISBN: (纸本)9783642158377

Record linkage has a long tradition in both the statistical and the computer science literature. We survey current approaches to the record linkage problem in a privacy-aware setting and contrast these with the more traditional literature. We also identify several important open questions that pertain to private record linkage from different perspectives.

关键词： privacy-preserving techniques

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PRAM Optimization Using an Evolutionary Algorithm

PRAM Optimization Using an Evolutionary Algorithm

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conference on privacy in statistical databases 2010

作者： Mares, Jordi Torra, Vicenc CSIC IIIA Bellaterra 08193 Catalonia Spain

ISBN: (纸本)9783642158377

PRAM (Post Randomization Method) was introduced in 1997 but it is still one of the least used methods in statistical categorical data protection. This fact is because of the difficulty to obtain a good transition matrix in order to obtain a good protection. In this paper, we describe how to obtain a better protection using an evolutionary algorithm with integrated information loss and disclosure risk measures to find the best matrix. We also provide experiments using a real dataset of 1000 records in order to empirically evaluate the application of this technique.

关键词： Information privacy and Security Evolutionary Algorithms Post Randomization Method Information Loss Disclosure Risk

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