A Hybrid Approach to Private Record Linkage

İnan, Ali; Kantarcıoğlu, Murat; Bertino, Elisa; Scannapieco, Monica

doi:10.1109/icde.2008.4497458

Cited by 83 publications

(70 citation statements)

References 23 publications

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“…Inan et al claim that cryptographic operations are often slow for use in practical record linkage of large data sets. Towards this end, they propose separating data records into small subsets and then perform linkage only within a subset using homomorphic encryption [22,23]. While encryption based techniques are known to provide stronger security and privacy guarantees, earlier works have disregarded its practical significance (or applied it in a limited manner) on grounds of heavy computation and communication complexity.…”

Section: Related Workmentioning

confidence: 99%

Linking Health Records for Federated Query Processing

Dewri

Ong

Thurimella

2016

Proceedings on Privacy Enhancing Technologies

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A federated query portal in an electronic health record infrastructure enables large epidemiology studies by combining data from geographically dispersed medical institutions. However, an individual's health record has been found to be distributed across multiple carrier databases in local settings. Privacy regulations may prohibit a data source from revealing clear text identifiers, thereby making it non-trivial for a query aggregator to determine which records correspond to the same underlying individual. In this paper, we explore this problem of privately detecting and tracking the health records of an individual in a distributed infrastructure. We begin with a secure set intersection protocol based on commutative encryption, and show how to make it practical on comparison spaces as large as 10 10 pairs. Using bigram matching, precomputed tables, and data parallelism, we successfully reduced the execution time to a matter of minutes, while retaining a high degree of accuracy even in records with data entry errors. We also propose techniques to prevent the inference of identifier information when knowledge of underlying data distributions is known to an adversary. Finally, we discuss how records can be tracked utilizing the detection results during query processing.

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Section: Related Workmentioning

confidence: 99%

Linking Health Records for Federated Query Processing

Dewri

Ong

Thurimella

2016

Proceedings on Privacy Enhancing Technologies

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“…Al-Lawati et al [7] proposed a secure three-party blocking protocol in 2005 which achieves highperformance PPRL by using secure hash encoding for computing the TF-IDF distance measure in a secure fashion. Inan et al [4] proposed a hybrid approach that combines generalization and cryptographic techniques to solve the PPRL problem in 2008. An approach to PPRL was proposed by Karakasidis et al [8] in 2011 a secure blocking based on phonetic encoding algorithms.…”

Section: Related Workmentioning

confidence: 99%

“…As to the approaches between two databases: In [3], the two-party private blocking (TPPB) method avoids the use of a third party and cryptographic techniques and instead trades off privacy for blocking quality. In [4], Inan et al suggest creating forming generalized hierarchies (FGH) for reducing the cost of PPRL. However, the forming hierarchies may cause the blocks over-generalization and reduce the accuracy of blocking.…”

Section: Introductionmentioning

confidence: 99%

Private Blocking Technique for Multi-party Privacy-Preserving Record Linkage

et al. 2017

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The process of matching and integrating records that relate to the same entity from one or more datasets is known as record linkage, and it has become an increasingly important subject in many application areas, including business, government and health system. The data from these areas often contain sensitive information. To prevent privacy breaches, ideally records should be linked in a private way such that no information other than the matching result is leaked in the process, and this technique is called privacy-preserving record linkage (PPRL). With the increasing data, scalability becomes the main challenge of PPRL, and many private blocking techniques have been developed for PPRL. They are aimed at reducing the number of record pairs to be compared in the matching process by removing obvious non-matching pairs without compromising privacy. However, most of them are designed for two databases and they vary widely in their ability to balance competing goals of accuracy, efficiency and security. In this paper, we propose a novel private blocking approach for PPRL based on dynamic k-anonymous blocking and Paillier cryptosystem which can be applied on two or multiple databases. In dynamic kanonymous blocking, our approach dynamically generates blocks satisfying k-anonymity and more accurate values to represent the blocks with varying k. We also propose a novel similarity measure method which performs on the numerical attributes and combines with Paillier cryptosystem to measure the similarity of two or more blocks in security, which provides strong privacy guarantees that none information reveals even collusion. Experiments conducted on a public dataset of voter registration records validate that our approach is scalable to large databases and keeps a high quality of blocking. We compare our method with other techniques and demonstrate the increases in security and accuracy.

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“…One approach is given in [20]. In order to make the blocking step efficient the proposal is to first k-anonymize [35] the database rows, then share them.…”

Section: Blockingmentioning

confidence: 99%