Privacy-preserving Record Linkage Techniques Research Articles

Towards automatic Privacy-Preserving Record Linkage: A Transfer Learning based classification step

Privacy-Preserving Record Linkage (PPRL) intends to identify records that match the same real-world entities across disparate data sources while preserving the privacy of the individual entities. To identify matching records across different data sources and still preserve the privacy of the information, PPRL needs to consider several restrictions due to privacy limitations. For instance, PPRL is executed over anonymized (or encrypted) data to avoid re-identification. Moreover, the classification step of PPRL does not have access to labeled information (indicating if a pair of records is a match) and an oracle (specialist) to label a few instances. These limitations make it hard to employ automatic classification techniques. Most PPRL techniques use a simple threshold (defined by a specialist) to define whether a pair of records represent the same real-world entity or not. To overcome these problems, we present a Transfer Learning-based unsupervised classification step to PPRL, which leverages the information available in public (or synthetic) datasets to train accurate classifiers in a privacy-preserving context. We evaluate our approach using real-world and synthetic data, and the results demonstrate that our unsupervised classification step is able to overcome the most used classification strategies in PPRL.

On the effectiveness of graph matching attacks against privacy-preserving record linkage.

Linking several databases containing information on the same person is an essential step of many data workflows. Due to the potential sensitivity of the data, the identity of the persons should be kept private. Privacy-Preserving Record-Linkage (PPRL) techniques have been developed to link persons despite errors in the identifiers used to link the databases without violating their privacy. The basic approach is to use encoded quasi-identifiers instead of plain quasi-identifiers for making the linkage decision. Ideally, the encoded quasi-identifiers should prevent re-identification but still allow for a good linkage quality. While several PPRL techniques have been proposed so far, Bloom filter-based PPRL schemes (BF-PPRL) are among the most popular due to their scalability. However, a recently proposed attack on BF-PPRL based on graph similarities seems to allow individuals’ re-identification from encoded quasi-identifiers. Therefore, the graph matching attack is widely considered a serious threat to many PPRL-approaches and leads to the situation that BF-PPRL schemes are rejected as being insecure. In this work, we argue that this view is not fully justified. We show by experiments that the success of graph matching attacks requires a high overlap between encoded and plain records used for the attack. As soon as this condition is not fulfilled, the success rate sharply decreases and renders the attacks hardly effective. This necessary condition does severely limit the applicability of these attacks in practice and also allows for simple but effective countermeasures.

Taxonomy of Attacks on Privacy-Preserving Record Linkage

Record linkage is the process of identifying records that corresponds to the same real-world entities across different databases. Due to the absence of unique entity identifiers, record linkage is often based on quasi-identifying values of entities (individuals) such as their names and addresses. However, regulatory ethical and legal obligations can limit the use of such personal information in the linkage process in order to protect the privacy and confidentiality of entities. Privacy-preserving record linkage (PPRL) aims to develop techniques that enable the linkage of records without revealing any sensitive or confidential information about the entities that are represented by these records. Over the past two decades various PPRL techniques have been proposed to securely link records between different databases by encrypting and/or encoding sensitive values. However, some PPRL techniques, such as popular Bloom filter encoding, have shown to be susceptible to privacy attacks. These attacks exploit the weaknesses of PPRL techniques by trying to reidentify encrypted and/or encoded sensitive values. In this paper we propose a taxonomy for analysing such attacks on PPRL where we categorise attacks across twelve dimensions, including different types of adversaries, different attack types, assumed knowledge of the adversary, the vulnerabilities of encoded and/or encrypted values exploited by an attack, and assessing the success of attacks. Our taxonomy can be used by data custodians to analyse the privacy risks associated with different PPRL techniques in terms of existing as well as potential future attacks on PPRL.

A methodological assessment of privacy preserving record linkage using survey and administrative data.

The National Center for Health Statistics (NCHS) links data from surveys to administrative data sources, but privacy concerns make accessing new data sources difficult. Privacy-preserving record linkage (PPRL) is an alternative to traditional linkage approaches that may overcome this barrier. However, prior to implementing PPRL techniques it is important to understand their effect on data quality. Results from PPRL were compared to results from an established linkage method, which uses unencrypted (plain text) identifiers and both deterministic and probabilistic techniques. The established method was used as the gold standard. Links performed with PPRL were evaluated for precision and recall. An initial assessment and a refined approach were implemented. The impact of PPRL on secondary data analysis, including match and mortality rates, was assessed. The match rates for all approaches were similar, 5.1% for the gold standard, 5.4% for the initial PPRL and 5.0% for the refined PPRL approach. Precision ranged from 93.8% to 98.9% and recall ranged from 98.7% to 97.8%, depending on the selection of tokens from PPRL. The impact of PPRL on secondary data analysis was minimal. The findings suggest PPRL works well to link patient records to the National Death Index (NDI) since both sources have a high level of non-missing personally identifiable information, especially among adults 65 and older who may also have a higher likelihood of linking to the NDI. The results from this study are encouraging for first steps for a statistical agency in the implementation of PPRL approaches, however, future research is still needed.

Secure Record Linkage of Large Health Data Sets: Evaluation of a Hybrid Cloud Model.

BackgroundThe linking of administrative data across agencies provides the capability to investigate many health and social issues with the potential to deliver significant public benefit. Despite its advantages, the use of cloud computing resources for linkage purposes is scarce, with the storage of identifiable information on cloud infrastructure assessed as high risk by data custodians.ObjectiveThis study aims to present a model for record linkage that utilizes cloud computing capabilities while assuring custodians that identifiable data sets remain secure and local.MethodsA new hybrid cloud model was developed, including privacy-preserving record linkage techniques and container-based batch processing. An evaluation of this model was conducted with a prototype implementation using large synthetic data sets representative of administrative health data.ResultsThe cloud model kept identifiers on premises and uses privacy-preserved identifiers to run all linkage computations on cloud infrastructure. Our prototype used a managed container cluster in Amazon Web Services to distribute the computation using existing linkage software. Although the cost of computation was relatively low, the use of existing software resulted in an overhead of processing of 35.7% (149/417 min execution time).ConclusionsThe result of our experimental evaluation shows the operational feasibility of such a model and the exciting opportunities for advancing the analysis of linkage outputs.

A Privacy Attack on Multiple Dynamic Match-key based Privacy-Preserving Record Linkage.

IntroductionOver the last decade, the demand for linking records about people across databases has increased in various domains. Privacy challenges associated with linking sensitive information led to the development of privacy-preserving record linkage techniques. The multiple dynamic match-key encoding approach recently proposed by Randall et al. (IJPDS, 2019) is such a technique aimed at providing enough privacy for linkage applications while obtaining high linkage quality. However, the use of this encoding in large databases can reveal frequency information that can allow the re-identification of encoded values.ObjectivesWe propose a frequency-based attack to evaluate the privacy guarantees of multiple dynamic match-key encoding. We then present two recommendations that can be used in this match-key encoding approach to prevent such a privacy attack.MethodsThe proposed attack analyses the frequency distributions of individual match-keys in order to identify the attributes used for each match-key, where we assume the adversary has access to a plain-text database with similar characteristics as the encoded database. We employ a set of statistical correlation tests to compare the frequency distributions of match-key values between the encoded and plain-text databases. Once the attribute combinations used for match-keys are discovered, we then re-identify encoded sensitive values by utilising a frequency alignment method. Next, we propose two recommendations; one to alter the original frequency distributions and another to make the frequency distributions uniform. Both will help to prevent frequency-based attacks.ResultsWe evaluate our privacy attack using two large real-world databases. The results show that in certain situations the attack can successfully re-identify a set of sensitive values encoded using the multiple dynamic match-key encoding approach. On the databases used in our experiments, the attack can re-identify plain-text values with a precision and recall of both up to 98%. Furthermore, we show that our proposed recommendations are able to make this attack harder to perform with only a small reduction in linkage quality.ConclusionsOur proposed privacy attack demonstrates the weaknesses of multiple match-key encoding that should be taken into consideration when linking databases that contain sensitive personal information. Our proposed recommendations ensure that the multiple dynamic match-key encoding approach can be used securely while retaining high linkage quality.

Incremental clustering techniques for multi-party Privacy-Preserving Record Linkage

Privacy-Preserving Record Linkage (PPRL) supports the integration of sensitive information from multiple datasets, in particular the privacy-preserving matching of records referring to the same entity. PPRL has gained much attention in many application areas, with the most prominent ones in the healthcare domain. PPRL techniques tackle this problem by conducting linkage on masked (encoded) values. Employing PPRL on records from multiple (more than two) parties/sources (multi-party PPRL, MP-PPRL) is an increasingly important but challenging problem that so far has not been sufficiently solved. Existing MP-PPRL approaches are limited to finding only those entities that are present in all parties thereby missing entities that match only in a subset of parties. Furthermore, previous MP-PPRL approaches face substantial scalability limitations due to the need of a large number of comparisons between masked records. We thus propose and evaluate new MP-PPRL approaches that find matches in any subset of parties and still scale to many parties. Our approaches maintain all matches within clusters, where these clusters are incrementally extended or refined by considering records from one party after the other. An empirical evaluation using multiple real datasets ranging from 3 to 26 parties each containing up to 5 million records validates that our protocols are efficient, and significantly outperform existing MP-PPRL approaches in terms of linkage quality and scalability.

Comparing Record Linkage methods for real-world perinatal and neonatal data without unique identifiers

BackgroundData on newborns is regularly linked for epidemiological research. However, hospital data often suffers from incomplete data. We report on a linkage of two population-covering administrative health databases containing neonatal and perinatal data without unique personal identifiers and with incomplete information in standard patient identifiers.
 GoalTo study the effects of a policy-induced change from linking a national database without standard patient identifiers to a privacy-preserving Record Linkage method, we compare the linkage system in use to clear-text and privacy-preserving Record Linkage techniques. We expected large proportions of missing identifiers since they are not needed for clinical practice. Therefore, we expected missing links caused by missing identifiers. To study the impact of these missing identifiers on these successful links, we compared several linkage methods. Furthermore, we study the variations of linkage success between hospitals.
 MethodsPerinatal and neonatal data from population-covering real-world administrative databases was linked using several variants of state of the art methods, including Privacy-preserving Record Linkage (PPRL) techniques such as multiple match keys and Bloom filter methods.
 Results We report on the variation of linkage results between the hospitals and give possible explanations for the differences.
 The resulting linkage success is reported for each method. The impact of incomplete data on linkage success for each method is documented.
 Finally, we report on the relative performance of the modified techniques compared to standard linkage procedures used in practice.
 ConclusionImplementing a record linkage system based on identifiers not required for clinical practice caused a large number of missing identifiers. Since this information is essential for successful clear-text and private linkage methods, emphasizing the need for documenting patient identifiers, especially in cases where auxiliary information (such as stable addresses, date of birth or health insurance numbers) are missing, is of central importance for implementing a privacy-preserving Record Linkage system.

Public Cloud: The Future of Record Linkage?

IntroductionBusinesses worldwide are increasingly adopting the storage, compute and analytical services provided by cloud computing. Yet, few operational linkage units are keeping pace with this world of technological change - most use legacy systems approaching their limits with the rapidly increasing size and range of datasets now required for linkage.
 Objectives and ApproachTo meet the demands of linkage for the near future, it is important that new solutions for linkage consider the services provided by public cloud infrastructure for compute, storage and analytics. We examined Platform as a Service (PaaS) offerings for use in the development of a cost-effective cloud model for scalable, privacy-preserving record linkage (PPRL). PPRL techniques were adapted to maximise the quality of linkage and to automate as much of the process as possible. Finally, a prototype was created to demonstrate the capabilities and potential of the model.
 ResultsWe present our cloud model for PPRL, a platform for record linkage that provides rapid scaling of resources to meet demand, and the results of how our prototype performed on massive datasets.
 Conclusion/ImplicationsThe application of record linkage using relatively inexpensive cloud infrastructure represents a significant step towards providing an efficient and scalable record linkage service to researchers and government. Larger datasets can be linked efficiently, including national or cross-jurisdictional datasets, with little investment in private infrastructure, and improved turnaround times for researchers.

Partial Agreements in Probabilistic Linkages

IntroductionRecord linkage units around the world use probabilistic linkage techniques for routine linkage of large datasets. It is widely known how probabilities are converted to agreement and disagreement weights for each field, yet there has been little exploration of the methodology to optimally convert field similarity scores into partial weights.
 Objectives and ApproachString similarity comparators such as Jaro-Winkler are commonly used in traditional linkage, other comparators such as the Sorenson Dice coefficient, Jaccard similarity and Hamming distance are used in alternative privacy-preserving record linkage techniques. Determining partial weights to apply at each level of similarity is a non-trivial task. However, both types of linkages would greatly benefit from similarity to weight functions for each field that maximises the accuracy of the linkage.
 We evaluated several methods for computing partial agreement weights and applied these to synthetic datasets with varying levels of corruption. We then evaluated the methods on real administrative datasets.
 ResultsExact comparisons can miss matches where typographical errors or misspellings produce small changes in value. Similarity comparisons can reduce the number of missed matches, but may also increase the number of incorrect matches.
 Various results of the partial agreement methods on Jaro-Winkler, Sorenson Dice coefficient, Jaccard similarity and Hamming distance comparators will be presented. A generic function to convert similarity values to weights, created from synthetic data, can be used on most datasets with a greatly improved result in linkage quality. However, maximising the linkage quality requires the creation of similarity-to-weight functions that are optimised for each dataset.
 Conclusion/ImplicationsAccuracy in record linkage is vital for the correct analysis of linked data. It is even more critical in privacy-preserving record linkage where the ability for clerical review is limited. Optimised functions for converting similarities to partial weights can significantly improve the quality of linkage and should not be overlooked.

Proof of Concept for a Privacy Preserving National Mortality Register

IntroductionNational mortality registers are essential for medical research. Therefore, most nations operate such registers. Due to the administrative structure and data protection legislation, there is no such registry in Germany. We demonstrate that a national mortality registry is technically feasible under the given constraints with privacy preserving record linkage (PPRL).
 Objectives and ApproachGetting the legal permission to operate a national mortality registry for research will be easier if the linkage can be done without revealing personal identifiers by using PPRL. To estimate precision and recall of different encodings, we used two settings: (1) matching a local mortality registry (n = 14,003) with mortality data of a university hospital (n = 2,466); (2) matching 1 million simulated records from a national database of names with a corrupted subset. This corresponds to a match of all deceased persons with the deceased persons in the largest federal state (n = 205,000).
 ResultsLinkage results for clear-text identifiers show very high recall and precision. Bloom-Filter based encryptions yield comparable results. Neither precision nor recall declines more than 2%. Phonetic codes yield high precision but low recall. Some variants of Bloom Filter-based encodings yield better results than probabilistic linkage on clear-text identifiers. This is mainly due to the rarely mentioned detail of using different passwords for different identifiers in the same Bloom Filter. Therefore, implementation details of Bloom Filters are more important than commonly thought. Overall, we recommend the use of salted Bloom Filter-based methods with different passwords for different identifiers to increase security and to prevent all known attacks on identifier encryptions.
 Conclusion/ImplicationsAlthough most PPRL techniques would yield acceptable results in the given setting of a national register, salted Bloom filter encodings are more secure against attacks while still showing high precision and recall. Therefore, we consider a national mortality register using only encrypted identifiers of deceased persons as feasible.

Evaluating privacy-preserving record linkage using cryptographic long-term keys and multibit trees on large medical datasets

BackgroundIntegrating medical data using databases from different sources by record linkage is a powerful technique increasingly used in medical research. Under many jurisdictions, unique personal identifiers needed for linking the records are unavailable. Since sensitive attributes, such as names, have to be used instead, privacy regulations usually demand encrypting these identifiers. The corresponding set of techniques for privacy-preserving record linkage (PPRL) has received widespread attention. One recent method is based on Bloom filters. Due to superior resilience against cryptographic attacks, composite Bloom filters (cryptographic long-term keys, CLKs) are considered best practice for privacy in PPRL. Real-world performance of these techniques using large-scale data is unknown up to now.MethodsUsing a large subset of Australian hospital admission data, we tested the performance of an innovative PPRL technique (CLKs using multibit trees) against a gold-standard derived from clear-text probabilistic record linkage. Linkage time and linkage quality (recall, precision and F-measure) were evaluated.ResultsClear text probabilistic linkage resulted in marginally higher precision and recall than CLKs. PPRL required more computing time but 5 million records could still be de-duplicated within one day. However, the PPRL approach required fine tuning of parameters.ConclusionsWe argue that increased privacy of PPRL comes with the price of small losses in precision and recall and a large increase in computational burden and setup time. These costs seem to be acceptable in most applied settings, but they have to be considered in the decision to apply PPRL. Further research on the optimal automatic choice of parameters is needed.

Privacy-Preserving Record Linkage: An international collaboration between Canada, Australia and Wales

ABSTRACT
 ObjectivesLinkage of “big data” can provide the answers to a variety of health questions that benefit the delivery of patient care, impact of policies, system planning and evaluation. In some jurisdictions, legal and operational barriers may prevent data linkage for research and system evaluation. Collaboration between international research institutions in Canada, Australia and Wales was formed at the Farr Institute International Conference in 2015. This partnership will test privacy-preserving record linkage (PPRL) techniques for linkage accuracy on real datasets held in a Canadian data repository.
 ApproachBloom filter PPRL techniques have been incorporated into a prototype linkage system. Evaluations on probabilistic linkage using Bloom filters method have shown potential for large-scale record linkage, performing both accurately and efficiently under experimental conditions. The prototype will be used to evaluate the Bloom filter PPRL techniques in 3 phases. Phase 1: 3 tests using simulated data relating to 20 million individuals will be matched to a sub-cohort of 1 million individuals. Phase 2: 100,000 people from hospital inpatient records will be matched to 18 million people in a health system registration file.
 These tests will inform whether the method can achieve high levels of privacy protection without negatively impacting performance and linkage quality. Performance indicators include match rate and processing efficiency based on record volumes.
 ResultsLinkage quality will be assessed by the number of true matches and non matches identified as links and non-links. This method will be evaluated using synthetic and real-world datasets, where the true match status is known. Initial performance testing linked a file of 3,000 records to 30,000 with a 100% match result. Subsequent test phases as above will continue to be evaluated and these results will be presented.
 ConclusionCompletion of the phased tests will confirm the ability to link datasets while preserving privacy. This international collaboration will expand the utility of this prototype linkage system and expand the global knowledge bank focusing on PPRL methods in general. It will also inform how to adapt to local requirements by providing a solution to many common legal and administrative challenges.

Secure Privacy Preserving Record Linkage of Large Databases by Modified Bloom Filter Encodings.

ABSTRACTObjectiveIn most European settings, record linkage across different institutions has to be based on personal identifiers such as names, birthday or place of birth. To protect the privacy of research subjects, the identifiers have to be encrypted. In practice, these identifiers show error rates up to 20% per identifier, therefore linking on encrypted identifiers usually implies the loss of large subsets of the databases. In many applications, this loss of cases is related to variables of interest for the subject matter of the study. Therefore, this kind of record-linkage will generate biased estimates.
 These problems gave rise to techniques of Privacy Preserving Record Linkage (PPRL). Many different PPRL techniques have been suggested within the last 10 years, very few of them are suitable for practical applications with large database containing millions of records as they are typical for administrative or medical databases. One proven technique for PPRL for large scale applications is PPRL based on Bloom filters.MethodUsing appropriate parameter settings, Bloom filter approaches show linkage results comparable to linkage based on unencrypted identifiers. Furthermore, this approach has been used in real-world settings with data sets containing up to 100 Million records. By the application of suitable blocking strategies, linking can be done in reasonable time.ResultHowever, Bloom filters have been subject of cryptographic attacks. Previous research has shown that the straight application of Bloom filters has a nonzero re-identification risk. We will present new results on recently developed techniques to defy all known attacks on PPRL Bloom filters. These computationally simple algorithms modify the identifiers by different cryptographic diffusion techniques. The presentation will demonstrate these new algorithms and show their performance concerning precision, recall and re-identification risk on large databases.

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.

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.