Methodology in our madness

It is a great pleasure to present the first issue of a new journal. However, the more sceptical reader might wonder whether yet another new journal is really needed. In this editorial we attempt to justify our self-indulgence and to set out our vision for Survey Research Methods

Multiscale modeling in biology

The 1966 science-fction film Fantastic Voyage captured the public imagination with a clever idea: what fantastic things might we see and do if we could minaturize ourselves and travel through the bloodstream as corpuscles do? (This being Hollywood, the answer was that we'd save a fellow scientist from evildoers.

Privacy-Preserving Record Linkage and Privacy-Preserving Blocking for Large Files with Cryptographic Keys using Multibit Trees

Increasingly, administrative data is being used for statistical purposes, for example registry based census taking. In practice, this usually requires linking separate files containing information on the same unit, without revealing the identity of the unit. If the linkage has to be done without a unique identification number, it is necessary to compare keys which are derived from unit identifiers and which are assumed to be similar. When dealing with large files like census data or population reg- istries, comparing each possible pair of keys of two files is impossible. Therefore, special algorithms (blocking methods) have to be used to reduce the number of comparisons needed. If the identifiers have to be encrypted due to privacy concerns, the number of available algorithms for blocking is very limited. This paper describes the adoption of a recently introduced algorithm for this problem and its performance for large files

An efficient Privacy-Preserving Record Linkage Technique for Administrative Data and Censuses

Increasingly, administrative data is being used for statistical purposes, such as for registry-based census taking. Due to privacy concerns, this often requires linking separate files containing information on the same unit without revealing the identity of the unit. If the linkage has to be done without a unique identification number, it is necessary to compare keys derived from personal identifiers. When dealing with large files such as census data, comparing each possible pair of keys for two files is impossible. Therefore, special algorithms (blocking methods) must be used to reduce the number of comparisons needed. If the identifiers have to be encrypted due to privacy concerns, the number of available algorithms for record linkage and blocking is very limited. This paper describes the combination of a recently introduced encryption method for identifiers with a novel algorithm for blocking. Simulations show that the performance of these techniques allows their use for Big Data applications, censuses and population registries

Randomized Response and Balanced Bloom Filters for Privacy Preserving Record Linkage

In most European settings, record linkage across different institutions is based on encrypted personal identifiers – such as names, birthdays, or places of birth – to protect privacy. However, in practice up to 20% of the records may contain errors in identifiers. Thus, exact record linkage on encrypted identifiers usually results in the loss of large subsets of the data. Such losses usually imply biased statistical estimates since the causes of errors might be correlated with the variables of interest in many applications. Over the past 10 years, the field of Privacy Preserving Record Linkage (PPRL) has developed different techniques to link data without revealing the identity of the described entity. However, only few techniques are suitable for applied research with large data bases that include millions of records, which is typical for administrative or medical data bases. Bloom filters were found to be one successful technique for PPRL when large scale applications are concerned. Yet, Bloom filters have been subject to cryptographic attacks. Previous research has shown that the straight application of Bloom filters has a non-zero re-identification risk. We present new results on recently developed techniques defying all known attacks on PPRL Bloom filters. The computationally inexpensive algorithms modify personal identifiers by combining different cryptographic techniques. The paper demonstrates these new algorithms and demonstrates their performance concerning pprecision, recall, and re-identification risk on large data bases