Some methods for blindfolded record linkage

BACKGROUND: The linkage of records which refer to the same entity in separate data collections is a common requirement in public health and biomedical research. Traditionally, record linkage techniques have required that all the identifying data in which links are sought be revealed to at least one party, often a third party. This necessarily invades personal privacy and requires complete trust in the intentions of that party and their ability to maintain security and confidentiality. Dusserre, Quantin, Bouzelat and colleagues have demonstrated that it is possible to use secure one-way hash transformations to carry out follow-up epidemiological studies without any party having to reveal identifying information about any of the subjects – a technique which we refer to as "blindfolded record linkage". A limitation of their method is that only exact comparisons of values are possible, although phonetic encoding of names and other strings can be used to allow for some types of typographical variation and data errors. METHODS: A method is described which permits the calculation of a general similarity measure, the n-gram score, without having to reveal the data being compared, albeit at some cost in computation and data communication. This method can be combined with public key cryptography and automatic estimation of linkage model parameters to create an overall system for blindfolded record linkage. RESULTS: The system described offers good protection against misdeeds or security failures by any one party, but remains vulnerable to collusion between or simultaneous compromise of two or more parties involved in the linkage operation. In order to reduce the likelihood of this, the use of last-minute allocation of tasks to substitutable servers is proposed. Proof-of-concept computer programmes written in the Python programming language are provided to illustrate the similarity comparison protocol. CONCLUSION: Although the protocols described in this paper are not unconditionally secure, they do suggest the feasibility, with the aid of modern cryptographic techniques and high speed communication networks, of a general purpose probabilistic record linkage system which permits record linkage studies to be carried out with negligible risk of invasion of personal privacy

Privacy-preserving record linkage using Bloom filters

<p>Abstract</p> <p>Background</p> <p>Combining multiple databases with disjunctive or additional information on the same person is occurring increasingly throughout research. If unique identification numbers for these individuals are not available, probabilistic record linkage is used for the identification of matching record pairs. In many applications, identifiers have to be encrypted due to privacy concerns.</p> <p>Methods</p> <p>A new protocol for privacy-preserving record linkage with encrypted identifiers allowing for errors in identifiers has been developed. The protocol is based on Bloom filters on <it>q</it>-grams of identifiers.</p> <p>Results</p> <p>Tests on simulated and actual databases yield linkage results comparable to non-encrypted identifiers and superior to results from phonetic encodings.</p> <p>Conclusion</p> <p>We proposed a protocol for privacy-preserving record linkage with encrypted identifiers allowing for errors in identifiers. Since the protocol can be easily enhanced and has a low computational burden, the protocol might be useful for many applications requiring privacy-preserving record linkage.</p

A Taxonomy of Privacy-Preserving Record Linkage Techniques

The process of identifying which records in two or more databases correspond to the same entity is an important aspect of data quality activities such as data pre-processing and data integration. Known as record linkage, data matching or entity resolution, this process has attracted interest from researchers in fields such as databases and data warehousing, data mining, information systems, and machine learning. Record linkage has various challenges, including scalability to large databases, accurate matching and classification, and privacy and confidentiality. The latter challenge arises because commonly personal identifying data, such as names, addresses and dates of birth of individuals, are used in the linkage process. When databases are linked across organizations, the issue of how to protect the privacy and confidentiality of such sensitive information is crucial to successful application of record linkage. In this paper we present an overview of techniques that allow the linking of databases between organizations while at the same time preserving the privacy of these data. Known as 'privacy-preserving record linkage' (PPRL), various such techniques have been developed. We present a taxonomy of PPRL techniques to characterize these techniques along 15 dimensions, and conduct a survey of PPRL techniques. We then highlight shortcomings of current techniques and discuss avenues for future research

An Efficient Two-Party Protocol for Approximate Matching in Private Record Linkage

The task of linking multiple databases with the aim to identify records that refer to the same entity is occurring increasingly in many application areas. If unique identifiers for the entities are not available in all the databases to be linked, techniques that calculate approximate similarities between records must be used for the identification of matching pairs of records. Often, the records to be linked contain personal information such as names and addresses. In many applications, the exchange of attribute values that contain such personal details between organisations is not allowed due to privacy concerns. The linking of records between databases without revealing the actual attribute values in these records is the research problem known as 'privacy-preserving record linkage' (PPRL).While various approaches have been proposed to deal with privacy within the record linkage process, a viable solution that is well applicable to real-world conditions needs to address the major aspect of scalability of linking very large databases while preserving security and linkage quality. We propose a novel two-party protocol for PPRL that addresses scalability, security and quality/ accuracy. The protocol is based on (1) the use of reference values that are available to both database owners, and allows them to individually calculate the similarities between their attribute values and the reference values; and (2) the binning of these calculated similarity values to allow their secure exchange between the two database owners. Experiments on a real-world database with nearly two million records yield linkage results that have a linear scalability to large databases and high linkage accuracy, allowing for approximate matching in the privacy-preserving context. Since the protocol has a low computational burden and allows quality approximate matching while still preserving the privacy of the databases that are matched, the protocol can be useful for many real-world applications requiring PPRL

Enhancing decision-making in user-centered web development: a methodology for card-sorting analysis

The World Wide Web has become a common platform for interactive software development. Most web applications feature custom user interfaces used by millions of people every day. Information architecture addresses the structural design of information to build quality web applications with improved usability of content, navigation, and findability. One of the most frequently utilized information architecture methods is card sorting—an affordable, user-centered approach for eliciting and evaluating categories and navigable items. Card sorting facilitates decision-making during the development process based on users’ mental models of a given application domain. However, although the qualitative analysis of card sorts has become common practice in information architecture, the quantitative analysis of card sorting is less widely applied. The reason for this gap is that quantitative analysis often requires the use of customized techniques to extract meaningful information for decision-making. To facilitate this process and support the structuring of information, we propose a methodology for the quantitative analysis of card-sorting results in this paper. The suggested approach can be systematically applied to provide clues and support for decisions. These might significantly impact the design and, thus, the final quality of the web application. Therefore, the approach includes proper goodness values that enable comparisons among the results of the methods and techniques used and ensure the suitability of the analyses performed. Two publicly available datasets were used to demonstrate the key issues related to the interpretation of card sorting results and the overall suitability and validity of the proposed methodologyThis work was partially supported by the Spanish Government [grant number RTI2018-095255-B-I00]; and the Madrid Research Council [Grant Number P2018/TCS-4314