Enhancing Access to Microdata While Protecting Confidentiality: Prospects for the Future

Duncan, George T.; Pearson, R. W.

doi:10.1214/ss/1177011681

Cited by 138 publications

(77 citation statements)

References 18 publications

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“…We describe this in Section 2.3 and use it in the empirical part. An alternative solution is in the use of synthetic data and a related notion of differential privacy, for example, Dwork and Nissim (2004), Dwork (2006), and Abowd and Vilhuber (2008), as well as Duncan and Lambert (1986), Duncan and Mukherjee (1991), Duncan and Pearson (1991), Fienberg (1994Fienberg ( , 2001, and Duncan, Fienberg, Krishnan, Padman, and Roehrig (2001), Abowd and Woodcock (2001), Kinney, Reiter, Reznek, Miranda, Jarmin, andAbowd (2011), andHu, Reiter, andWang (2014), among others.…”

Section: Related Literaturementioning

confidence: 99%

Identification, data combination, and the risk of disclosure

Komarova¹,

Nekipelov

Yakovlev

2018

Quantitative Economics

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It is commonplace that the data needed for econometric inference are not contained in a single source. In this paper we analyze the problem of parametric inference from combined individual-level data when data combination is based on personal and demographic identifiers such as name, age, or address. Our main question is the identification of the econometric model based on the combined data when the data do not contain exact individual identifiers and no parametric assumptions are imposed on the joint distribution of information that is common across the combined data set. We demonstrate the conditions on the observable marginal distributions of data in individual data sets that can and cannot guarantee identification of the parameters of interest. We also note that the data combination procedure is essential in a semiparametric setting such as ours. Provided that the (nonparametric) data combination procedure can only be defined in finite samples, we introduce a new notion of identification based on the concept of limits of statistical experiments. Our results apply to the setting where the individual data used for inferences are sensitive and their combination may lead to a substantial increase in the data sensitivity or lead to a "de-anonymization" of the previously "anonymized" information. We demonstrate that the point identification of an econometric model from combined data is incompatible with restrictions on the risk of individual disclosure. If the data combination procedure guarantees a bound on the risk of individual disclosure, then the information available from the combined data set allows one to identify the parameter of interest only partially, and the size of the identification region is inversely related to the upper bound guarantee for the disclosure risk. This result is new in the context of data combination as we notice that the quality of links that need to be used in the combined data to assure point identification may be much higher than the average link quality in the entire data set, and thus point inference requires the use of the 396 Komarova, Nekipelov, and Yakovlev Quantitative Economics 9 (2018) most sensitive subset of the data. Our results provide important insights into the ongoing discourse on the empirical analysis of merged administrative records as well as discussions on the "disclosive" nature of policies implemented by the datadriven companies (such as internet services companies and medical companies using individual patient records for policy decisions).

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Section: Related Literaturementioning

confidence: 99%

Identification, data combination, and the risk of disclosure

Komarova¹,

Nekipelov

Yakovlev

2018

Quantitative Economics

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“…In technical terms disclosure limitation involves manipulating or masking a data matrix. Following important work by Cox (1980), Dalenius and Reiss (1982), Roberts (1986), Dalenius (1986), and Duncan and Lambert (1986), Duncan and Pearson (1991) provide a clear and accessible description of the methods available for the reduction of disclosure risk. More recent monographic treatments by de Waal (1996, 2001) and Doyle et al (2001) update the framework to emphasize disclosure risk assessment and administrative alternatives to statistical solutions.…”

Section: Disclosure Limitation Basicsmentioning

confidence: 99%

“…These practices --briefly introduced and documented in this paper --constitute the starting point for all assessments of disclosure risk and implementations of disclosure limitation strategies. Those who work on data sharing are well advised to use those procedures as an explicit checklist, starting with the work of the Subcommittee on Disclosure Avoidance Techniques (1978Techniques ( , 1994, continuing with the work of Duncan and Pearson (1991), and including the recent recommendations from Armstrong, Rushton, and Zimmerman (1999); Doyle et al (2001);and Willenbor and de Waal (2001). The Checklist on Disclosure Potential of Proposed Data Releases (Interagency Confidentiality and Data Access Group 1999) is a starting point for the verification of these processes, which our research on best practices is designed to complement and enhance.…”

Section: Best Practices and Recommendationsmentioning

confidence: 99%

Providing Spatial Data for Secondary Analysis: Issues and Current Practices Relating to Confidentiality

Gutmann

Witkowski

Colyer

et al. 2008

Popul Res Policy Rev

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Spatially explicit data pose a series of opportunities and challenges for all the actors involved in providing data for long-term preservation and secondary analysis --the data producer, the data archive, and the data user. We report on opportunities and challenges for each of the three players, and then turn to a summary of current thinking about how best to prepare, archive, disseminate, and make use of social science data that have spatially explicit identification. The core issue that runs through the paper is the risk of the disclosure of the identity of respondents. If we know where they live, where they work, or where they own property, it is possible to find out who they are. Those involved in collecting, archiving, and using data need to be aware of the risks of disclosure and become familiar with best practices to avoid disclosures that will be harmful to respondents.

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“…With more and more sensitive and confidential information stored, shared and manipulated at digital level [3], individuals and organisations expect appropriate measures to ensure privacy of such information. Recent research indicates that the protection of users' privacy has become crucial for organisations in order to maintain a high standard of services and retain competitive advantage [4].…”

Section: Introductionmentioning

confidence: 99%

Visual Privacy Management in User Centric Open Environments

Diamantopoulou

Pavlidis

2017

2017 11th International Conference on Research Challenges in Information Science (RCIS)

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Abstract-In open and dynamic online services the exchange of information is demanded to be easy, simple and always available. However, potential users of online services are still reluctant to outsource sensitive data to these services, mainly due to lack of control over management and privacy issues of their data. This becomes more complex when dealing with Public Administrations (PAs) which handle data of citizens, where the latter are obliged, in many cases by law, to do so. This paper presents the VisiOn Privacy Platform, which analyses privacy preferences, and introduces the concept of the Privacy Level Agreement, capturing the PAs and citizens privacy requirements, thus supporting transparency and accountability for PAs.

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Enhancing Access to Microdata While Protecting Confidentiality: Prospects for the Future

Cited by 138 publications

References 18 publications

Identification, data combination, and the risk of disclosure

Identification, data combination, and the risk of disclosure

Providing Spatial Data for Secondary Analysis: Issues and Current Practices Relating to Confidentiality

Visual Privacy Management in User Centric Open Environments

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