Quality Indicators for Statistical Disclosure Methods: A Case Study on the Structure of Earnings Survey

Templ, Matthias

doi:10.1515/jos-2015-0043

Cited by 7 publications

(3 citation statements)

References 41 publications

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“…Utility measures specialized in a particular field should always be preferred to general measures ([ 42 ]; eg, as implemented in sdcMicro). To check the data utility after anonymization, visual comparisons of the original nonanonymized and anonymized data sets, as well as chi-square tests comparing contingency tables obtained from original and anonymized data, are shown.…”

Section: Resultsmentioning

confidence: 99%

Privacy of Study Participants in Open-access Health and Demographic Surveillance System Data: Requirements Analysis for Data Anonymization

Templ

Kanjala²,

Siems

2022

JMIR Public Health Surveill

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Background Data anonymization and sharing have become popular topics for individuals, organizations, and countries worldwide. Open-access sharing of anonymized data containing sensitive information about individuals makes the most sense whenever the utility of the data can be preserved and the risk of disclosure can be kept below acceptable levels. In this case, researchers can use the data without access restrictions and limitations. Objective This study aimed to highlight the requirements and possible solutions for sharing health surveillance event history data. The challenges lie in the anonymization of multiple event dates and time-varying variables. Methods A sequential approach that adds noise to event dates is proposed. This approach maintains the event order and preserves the average time between events. In addition, a nosy neighbor distance-based matching approach to estimate the risk is proposed. Regarding the key variables that change over time, such as educational level or occupation, we make 2 proposals: one based on limiting the intermediate statuses of the individual and the other to achieve k-anonymity in subsets of the data. The proposed approaches were applied to the Karonga health and demographic surveillance system (HDSS) core residency data set, which contains longitudinal data from 1995 to the end of 2016 and includes 280,381 events with time-varying socioeconomic variables and demographic information. Results An anonymized version of the event history data, including longitudinal information on individuals over time, with high data utility, was created. Conclusions The proposed anonymization of event history data comprising static and time-varying variables applied to HDSS data led to acceptable disclosure risk, preserved utility, and being sharable as public use data. It was found that high utility was achieved, even with the highest level of noise added to the core event dates. The details are important to ensure consistency or credibility. Importantly, the sequential noise addition approach presented in this study does not only maintain the event order recorded in the original data but also maintains the time between events. We proposed an approach that preserves the data utility well but limits the number of response categories for the time-varying variables. Furthermore, using distance-based neighborhood matching, we simulated an attack under a nosy neighbor situation and by using a worst-case scenario where attackers have full information on the original data. We showed that the disclosure risk is very low, even when assuming that the attacker’s database and information are optimal. The HDSS and medical science research communities in low- and middle-income country settings will be the primary beneficiaries of the results and methods presented in this paper; however, the results will be useful for anyone working on anonymizing longitudinal event history data with time-varying variables for the purposes of sharing.

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Section: Resultsmentioning

confidence: 99%

Privacy of Study Participants in Open-access Health and Demographic Surveillance System Data: Requirements Analysis for Data Anonymization

Templ

Kanjala²,

Siems

2022

JMIR Public Health Surveill

Self Cite

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“…comparing contingency tables, output from regression models, distributions, point and variance estimates, etc. Templ [76] argues that rather data-and use-case-depended measures should be used instead of general purpose measures (such as means and correlations).…”

Section: Anonymization For Sharing Public-use Files (Cpo)mentioning

confidence: 99%

A systematic overview on methods to protect sensitive data provided for various analyses

Templ

Sariyar

2022

Int. J. Inf. Secur.

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In view of the various methodological developments regarding the protection of sensitive data, especially with respect to privacy-preserving computation and federated learning, a conceptual categorization and comparison between various methods stemming from different fields is often desired. More concretely, it is important to provide guidance for the practice, which lacks an overview over suitable approaches for certain scenarios, whether it is differential privacy for interactive queries, k-anonymity methods and synthetic data generation for data publishing, or secure federated analysis for multiparty computation without sharing the data itself. Here, we provide an overview based on central criteria describing a context for privacy-preserving data handling, which allows informed decisions in view of the many alternatives. Besides guiding the practice, this categorization of concepts and methods is destined as a step towards a comprehensive ontology for anonymization. We emphasize throughout the paper that there is no panacea and that context matters.

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“…An important goal, however, should always be that the difference in results of the most important statistics based on anonymized and original data should be very small or even zero. Thus, the goal is to measure data utility based on benchmarking indicators (Ichim and Franconi 2010;Templ 2011b), which is in general a better approach to assess data quality than applying general tools.…”

Section: A Note On Other Measures Of Data Utilitymentioning

confidence: 99%

Statistical Disclosure Control for Micro-Data Using theRPackagesdcMicro

2015

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The demand for data from surveys, censuses or registers containing sensible information on people or enterprises has increased significantly over the last years. However, before data can be provided to the public or to researchers, confidentiality has to be respected for any data set possibly containing sensible information about individual units. Confidentiality can be achieved by applying statistical disclosure control (SDC) methods to the data in order to decrease the disclosure risk of data.The R package sdcMicro serves as an easy-to-handle, object-oriented S4 class implementation of SDC methods to evaluate and anonymize confidential micro-data sets. It includes all popular disclosure risk and perturbation methods. The package performs automated recalculation of frequency counts, individual and global risk measures, information loss and data utility statistics after each anonymization step. All methods are highly optimized in terms of computational costs to be able to work with large data sets. Reporting facilities that summarize the anonymization process can also be easily used by practitioners. We describe the package and demonstrate its functionality with a complex household survey test data set that has been distributed by the International Household Survey Network.

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Quality Indicators for Statistical Disclosure Methods: A Case Study on the Structure of Earnings Survey

Cited by 7 publications

References 41 publications

Privacy of Study Participants in Open-access Health and Demographic Surveillance System Data: Requirements Analysis for Data Anonymization

Privacy of Study Participants in Open-access Health and Demographic Surveillance System Data: Requirements Analysis for Data Anonymization

A systematic overview on methods to protect sensitive data provided for various analyses

Statistical Disclosure Control for Micro-Data Using theRPackagesdcMicro

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