Abstract
Record linkage is an emerging research area which is required by various real-world applications to identify which records in different data sources refer to the same real-world entities. Often privacy concerns and restrictions prevent the use of traditional record linkage applications across different organizations. Linking records in situations where no private or confidential information can be revealed is known as privacy-preserving record linkage (PPRL). As with traditional record linkage applications, scalability is a main challenge in PPRL. This challenge is generally addressed by employing a blocking technique that aims to reduce the number of candidate record pairs by removing record pairs that likely refer to non-matches without comparing them in detail. This paper presents an efficient private blocking technique based on a sorted neighborhood approach that combines k-anonymous clustering and the use of public reference values. An empirical study conducted on real-world databases shows that this approach is scalable to large databases, and that it can provide effective blocking while preserving k-anonymous characteristics. The proposed approach can be up-to two orders of magnitude faster than two state-of-the-art private blocking techniques, k-nearest neighbor clustering and Hamming based locality sensitive hashing.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Christen, P.: Data Matching. Data-Centric Systems and Appl. Springer (2012)
Batini, C., Scannapieca, M.: Data quality: Concepts, methodologies and techniques. In: Data-Centric Systems and Appl. Springer (2006)
Christen, P.: A survey of indexing techniques for scalable record linkage and deduplication. IEEE Transactions on Knowledge and Data Engineering 12(9) (2012)
Vatsalan, D., Christen, P., Verykios, V.: A taxonomy of privacy-preserving record linkage techniques. Information Systems (2013)
Hall, R., Fienberg, S.: Privacy-preserving record linkage. In: Domingo-Ferrer, J., Magkos, E. (eds.) PSD 2010. LNCS, vol. 6344, pp. 269–283. Springer, Heidelberg (2010)
Churches, T., Christen, P.: Blind data linkage using n-gram similarity comparisons. In: Dai, H., Srikant, R., Zhang, C. (eds.) PAKDD 2004. LNCS (LNAI), vol. 3056, pp. 121–126. Springer, Heidelberg (2004)
Fellegi, I.P., Sunter, A.B.: A theory for record linkage. Journal of the American Statistical Society 64(328), 1183–1210 (1969)
Jin, L., Li, C., Mehrotra, S.: Efficient record linkage in large data sets. In: DASFAA 2003, pp. 137–146 (2003)
Cohen, W.W., Richman, J.: Learning to match and cluster large high-dimensional data sets for data integration. In: ACM SIGKDD, pp. 475–480 (2002)
Kim, H., Lee, D.: Harra: fast iterative hashed record linkage for large-scale data collections. In: EDBT, Lausanne, Switzerland, pp. 525–536 (2010)
Hernandez, M.A., Stolfo, S.J.: Real-world data is dirty: Data cleansing and the merge/purge problem. Data Mining and Knowledge Discovery 2(1), 9–37 (1998)
Draisbach, U., Naumann, F., Szott, S., Wonneberg, O.: Adaptive windows for duplicate detection. In: ICDE, pp. 1073–1083 (2012)
Sweeney, L.: K-anonymity: A model for protecting privacy. International Journal of Uncertainty Fuzziness and Knowledge Based Systems 10(5), 557–570 (2002)
Pang, C., Gu, L., Hansen, D., Maeder, A.: Privacy-preserving fuzzy matching using a public reference table. In: McClean, S., Millard, P., El-Darzi, E., Nugent, C. (eds.) Intelligent Patient Management. Studies in Computational Intelligence, vol. 189, pp. 71–89. Springer, Heidelberg (2009)
Karakasidis, A., Verykios, V.: Reference table based k-anonymous private blocking. In: ACM Symposium on Applied Computing, Riva del Garda, Italy (2012)
Durham, E.: A framework for accurate, efficient private record linkage. PhD thesis, Vanderbilt University (2012)
Al-Lawati, A., Lee, D., McDaniel, P.: Blocking-aware private record linkage. In: IQIS, pp. 59–68 (2005)
Inan, A., Kantarcioglu, M., Bertino, E., Scannapieco, M.: A hybrid approach to private record linkage. In: IEEE ICDE, Cancun, Mexico, pp. 496–505 (2008)
Inan, A., Kantarcioglu, M., Ghinita, G., Bertino, E.: Private record matching using differential privacy. In: EDBT (2010)
Karakasidis, A., Verykios, V., Christen, P.: Fake injection strategies for private phonetic matching. In: DPM, Leuven, Belgium (2011)
Vatsalan, D., Christen, P., Verykios, V.: An efficient two-party protocol for approximate matching in private record linkage. In: AusDM, CRPIT 121 (2011)
Scannapieco, M., Figotin, I., Bertino, E., Elmagarmid, A.: Privacy preserving schema and data matching. In: ACM SIGMOD, pp. 653–664 (2007)
Yakout, M., Atallah, M., Elmagarmid, A.: Efficient private record linkage. In: IEEE ICDE, Shanghai, pp. 1283–1286 (2009)
Schnell, R., Bachteler, T., Reiher, J.: Privacy-preserving record linkage using Bloom filters. BMC Medical Informatics and Decision Making 9(1) (2009)
Christen, P., Pudjijono, A.: Accurate synthetic generation of realistic personal information. In: Theeramunkong, T., Kijsirikul, B., Cercone, N., Ho, T.-B. (eds.) PAKDD 2009. LNCS (LNAI), vol. 5476, pp. 507–514. Springer, Heidelberg (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Vatsalan, D., Christen, P. (2013). Sorted Nearest Neighborhood Clustering for Efficient Private Blocking. In: Pei, J., Tseng, V.S., Cao, L., Motoda, H., Xu, G. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2013. Lecture Notes in Computer Science(), vol 7819. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37456-2_29
Download citation
DOI: https://doi.org/10.1007/978-3-642-37456-2_29
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-37455-5
Online ISBN: 978-3-642-37456-2
eBook Packages: Computer ScienceComputer Science (R0)