publications

2023

1. Stud. Health
   Technol. Inform.

   Implementing Informative-Based Active Learning in Biomedical Record Linkage for the Splink Package in Python

   Marko Miletic ,  and  Murat Sariyar

   Jun 2023

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   In biomedical record linkage, efficient determination of a threshold to decide at which level of similarity two records should be classified as belonging to the same patient is frequently still an open issue. Here, we describe how to implement an efficient active learning strategy that puts into practice a measure of usefulness of training sets for such a task. Our results show that active learning should always be considered when training data is to be produced via manual labeling. In addition to that, active learning gives a quick indication how complex a problem is by looking into the label frequencies: If the most difficult entities are always stemming from the same class, then the classifier will probably have less problems in distinguishing the classes. In big data applications, these two properties are essential, as the problems of under- and overfitting are exacerbated in such contexts.

2022

1. Int. J. Inf. Secur.

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

   Matthias Templ ,  and  Murat Sariyar

   International Journal of Information Security, Aug 2022

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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.

2017

1. J. Stat. Softw.

   Simulation of Synthetic Complex Data: The R Package simPop

   Matthias Templ ,  Bernhard Meindl ,  Alexander Kowarik ,  and  Olivier Dupriez

   Journal of Statistical Software, Aug 2017

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   The production of synthetic datasets has been proposed as a statistical disclosure control solution to generate public use files out of protected data, and as a tool to create "augmented datasets" to serve as input for micro-simulation models. Synthetic data have become an important instrument for ex-ante assessments of policy impact. The performance and acceptability of such a tool relies heavily on the quality of the synthetic populations, i.e., on the statistical similarity between the synthetic and the true population of interest. Multiple approaches and tools have been developed to generate synthetic data. These approaches can be categorized into three main groups: synthetic reconstruction, combinatorial optimization, and model-based generation. We provide in this paper a brief overview of these approaches, and introduce simPop, an open source data synthesizer. simPop is a user-friendly R package based on a modular object-oriented concept. It provides a highly optimized S4 class implementation of various methods, including calibration by iterative proportional fitting and simulated annealing, and modeling or data fusion by logistic regression. We demonstrate the use of simPop by creating a synthetic population of Austria, and report on the utility of the resulting data. We conclude with suggestions for further development of the package.

2015

1. J. Stat. Softw.

   Statistical Disclosure Control for Micro-Data Using the R Package sdcMicro

   Matthias Templ ,  Alexander Kowarik ,  and  Bernhard Meindl

   Journal of Statistical Software, Aug 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.

2011

1. Stat. Methods Appt.

   Simulation of close-to-reality population data for household surveys with application to EU-SILC

   Andreas Alfons ,  Stefan Kraft ,  Matthias Templ ,  and  Peter Filzmoser

   Statistical Methods & Applications, Apr 2011

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   Statistical simulation in survey statistics is usually based on repeatedly drawing samples from population data. Furthermore, population data may be used in courses on survey statistics to explain issues regarding, e.g., sampling designs. Since the availability of real population data is in general very limited, it is necessary to generate synthetic data for such applications. The simulated data need to be as realistic as possible, while at the same time ensuring data confidentiality. This paper proposes a method for generating close-to-reality population data for complex household surveys. The procedure consists of four steps for setting up the household structure, simulating categorical variables, simulating continuous variables and splitting continuous variables into different components. It is not required to perform all four steps so that the framework is applicable to a broad class of surveys. In addition, the proposed method is evaluated in an application to the European Union Statistics on Income and Living Conditions (EU-SILC).

2. J. Biomed. Inform.

   Controlling false match rates in record linkage using extreme value theory

   Murat Sariyar ,  Andreas Borg ,  and  Klaus Pommerening

   Journal of Biomedical Informatics, Aug 2011

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   Cleansing data from synonyms and homonyms is a relevant task in fields where high quality of data is crucial, for example in disease registries and medical research networks. Record linkage provides methods for minimizing synonym and homonym errors thereby improving data quality. We focus our attention to the case of homonym errors (in the following denoted as ‘false matches’), in which records belonging to different entities are wrongly classified as equal. Synonym errors (‘false non-matches’) occur when a single entity maps to multiple records in the linkage result. They are not considered in this study because in our application domain they are not as crucial as false matches. False match rates are frequently computed manually through a clerical review, so without modelling the distribution of the false match rates a priori. An exception is the work of Belin and Rubin (1995) [4]. They propose to estimate the false match rate by means of a normal mixture model that needs training data for a calibration process. In this paper we present a new approach for estimating the false match rate within the framework of Fellegi and Sunter by methods of Extreme Value Theory (EVT). This approach needs no training data for determining the threshold for matches and therefore leads to a significant cost-reduction. After giving two different definitions of the false match rate, we present the tools of the EVT used in this paper: the generalized Pareto distribution and the mean excess plot. Our experiments with real data show that the model works well, with only slightly lower accuracy compared to a procedure that has information about the match status and that maximizes the accuracy.

2010

1. Lect. Notes
   Comput. Sci.

   Disclosure Risk of Synthetic Population Data with Application in the Case of EU-SILC

   Matthias Templ ,  and  Andreas Alfons

   Aug 2010

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   In survey statistics, simulation studies are usually performed by repeatedly drawing samples from population data. Furthermore, population data may be used in courses on survey statistics to support the theory by practical examples. However, real population data containing the information of interest are in general not available, therefore synthetic data need to be generated. Ensuring data confidentiality is thereby absolutely essential, while the simulated data should be as realistic as possible. This paper briefly outlines a recently proposed method for generating close-to-reality population data for complex (household) surveys, which is applied to generate a population for Austrian EU-SILC (European Union Statistics on Income and Living Conditions) data. Based on this synthetic population, confidentiality issues are discussed using five different worst case scenarios. In all scenarios, the intruder has the complete information on key variables from the real survey data. It is shown that even in these worst case scenarios the synthetic population data are confidential. In addition, the synthetic data are of high quality.

2. R J.

   The RecordLinkage Package: Detecting Errors in Data

   Murat Sariyar ,  and  Andreas Borg

   The R Journal, Aug 2010

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   Abstract Record linkage deals with detecting homonyms and mainly synonyms in data. The package RecordLinkage provides means to perform and evaluate different record linkage methods. A stochas tic framework is implemented which calculates weights through an EM algorithm. The determination of the necessary thresholds in this model can be achieved by tools of extreme value theory. Further more, machine learning methods are utilized, including decision trees (rpart), bootstrap aggregating (bagging), ada boost (ada), neural nets (nnet) and support vector machines (svm). The generation of record pairs and comparison patterns from single data items are provided as well. Comparison patterns can be chosen to be binary or based on some string metrics. In order to reduce computation time and memory usage, blocking can be used. Future development will concentrate on additional and refined methods, performance improvements and input/output facilities needed for real-world application.