A taxonomy of privacy-preserving record linkage techniques
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When there are errors or missing address data, linkage quality suffers.
“When there are errors or missing address data, linkage quality suffers.84”
From The quality of social determinants data in the electronic health record: a systematic review (2021) · cites this paper as [84]
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Self-citationEvaluation of the encryption procedure and record linkage in the Belgian national cancer registry
Eycken, E. V., Haustermans, K. M., Buntinx, F., Ceuppens, A. M., Weyler, J., Wauters, E., Oyen, H. J. V., Schaever, M. D., Berge, D. V. D., & Haelterman, M. (2000) · Document Server@UHasselt (UHasselt) · Cited by 28 · see citing works
Privacy-Preserving Data Linkage and Geocoding: Current Approaches and Research Directions
Christen, P. (2006) · IEEE ... International Conference on Data Mining workshops · doi:10.1109/icdmw.2006.135 · Cited by 25 · see citing works
Self-citationAn iterative two-party protocol for scalable privacy-preserving record linkage
Vatsalan, D., & Christen, P. (2012) · ANU Open Research (Australian National University) · Cited by 23 · see citing works
Self-citationOn the Use of Semantic Blocking Techniques for Data Cleansing and Integration
Nin, J., Muntés-Mulero, V., Martínez-Bazán, N., & Larriba-Pey, J.-L. (2007) · doi:10.1109/ideas.2007.36 · Cited by 23 · see citing works
Information sharing across private databases
Agrawal, R., Evfimievski, A., & Srikant, R. (2003) · ACM SIGMOD International Conference on Management of Data (SIGMOD) · doi:10.1145/872769.872771 · Cited by 22 · see citing works
A privacy preserving efficient protocol for semantic similarity join using long string attributes
Hawashin, B., Fotouhi, F. A., & Truţă, T. M. (2011) · doi:10.1145/1971690.1971696 · Cited by 20 · see citing works
Approaches to Multiple Record Linkage
Sadinle, M., & Fienberg, S. E. (2011) · Cited by 16 · see citing works
Geocode Matching and Privacy Preservation
Christen, P. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-01718-6_2 · Cited by 14 · see citing works
Self-citationOn the Use of Semantic Blocking Techniques for Data Cleansing and Integration
Nin, J., Muntés-Mulero, V., Martínez-Bazán, N., & Larriba-Pey, J.-L. (2007) · International Database Engineering and Applications Symposium (IDEAS) · doi:10.1109/ideas.2007.4318104 · Cited by 14 · see citing works
Irreversible encryption method by generation of polynomials
Quantin, C., Bouzelat, H., & Dusserre, L. (1996) · Medical Informatics · doi:10.3109/14639239608995013 · Cited by 14 · see citing works
A Privacy Preserving Framework for Accuracy and Completeness Quality Assessment
Stella, F. A., Maurino, A., Batini, C., & Barone, D. (2009) · BOA (University of Milano-Bicocca) · Cited by 12 · see citing works
Privacy Preserving Group Linkage
Li, F., Chen, Y., Luo, B., Lee, D., & Liu, P. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-22351-8_27 · Cited by 11 · see citing works
Advances in Privacy Preserving Record Linkage
Karakasidis, A., & Verykios, V. S. (2011) · Advances in web technologies and engineering book series · doi:10.4018/978-1-61520-871-5.ch003 · Cited by 7 · see citing works
Self-citationFürnkranz, J., Gamberger, D., & Nada Lavrač, A. (2012) · Cognitive technologies · doi:10.1007/978-3-540-75197-7_7 · Cited by 5 · see citing works
Flagged first, then by citations
8 of the 147 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Sensing as a service model for smart cities supported by Internet of Things
Perera, C., Zaslavsky, A., Christen, P., & Georgakopoulos, D. (2014) · Transactions on Emerging Telecommunications Technologies · doi:10.1002/ett.2704 · Cited by 991 · see citing works
“Techniques similar to privacy preserving data sharing ad matching [40] need to be developed in order to combine sensor data to anonymize entities / profiles (excluding sensitive data) later at the server level.”
Self-citationHardy, S., Henecka, W., Ivey-Law, H., Nock, R., Patrini, G., Smith, G., & Thorne, B. (2017) · arXiv · doi:10.48550/arxiv.1711.10677 · Cited by 431 · see citing works
“CLKs, resulting in a number of comparisons equal to the product of the datasets sizes. The most similar pairs are selected as matches, in a greedy fashion. Faster computation is possible by blocking [Vatsalan et al., 2013b]. The outputs of entity matching are two permutations ˙;˝and a mask m: ˙;˝describe how A and B m…”
BackgroundInfluentialChallenges in administrative data linkage for research
Harron, K. L., Dibben, C. J. L., Boyd, J., Hjern, A., Azimaee, M., Barreto, M. L., & Goldstein, H. (2017) · Big Data & Society · doi:10.1177/2053951717745678 · Cited by 360 · see citing works
“Achieving a balance between data protection and accuracy and usability of the resulting linked dataset is an ongoing area of research (Vatsalan et al., 2013).”
BackgroundBig Data sources and methods for social and economic analyses
Blazquez-Soriano, A., & Domènech, J. (2017) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2017.07.027 · Cited by 344 · see citing works
A note on using the F-measure for evaluating record linkage algorithms
Hand, D., & Christen, P. (2018) · Statistics and Computing · doi:10.1007/s11222-017-9746-6 · Cited by 247 · see citing works
Self-citationBlocking and Filtering Techniques for Entity Resolution
Papadakis, G., Skoutas, D., Thanos, E., & Palpanas, T. (2020) · ACM Computing Surveys · doi:10.1145/3377455 · Cited by 212 · see citing works
“In contrast, Blocking constitutes an integral part of privacy-preserving ER, with several relevant works (for details, refer to a recent survey [165]).”
BackgroundPrivacy-Preserving Record Linkage for Big Data: Current Approaches and Research Challenges
Vatsalan, D., Sehili, Z., Christen, P., & Rahm, E. (2017) · Springer eBooks · doi:10.1007/978-3-319-49340-4_25 · Cited by 163 · see citing works
“Privacy: The privacy-preserving requirement in the record linkage process adds a third challenge, privacy, to the two main challenges of scalability and linkage quality [165].”
BackgroundMethodInfluentialSelf-citationThe quality of social determinants data in the electronic health record: a systematic review
Cook, L. A., Sachs, J., & Weiskopf, N. G. (2021) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocab199 · Cited by 153 · see citing works
“When there are errors or missing address data, linkage quality suffers.(84) Many of the social determinants data elements most commonly used in research, such as race, ethnicity, insurance status, and address, were originally collected as demographic data for administrative purposes.”
BackgroundChevrier, R., Foufi, V., Gaudet-Blavignac, C., Robert, A., & Lovis, C. (2019) · Journal of Medical Internet Research · doi:10.2196/13484 · Cited by 150 · see citing works
“Cryptography, privacy-preserving record linkage [17], and differential privacy [18] are among these techniques.”
Privacy-preserving record linkage on large real world datasets.
Randall, S. M., Ferrante, A. M., Boyd, J. H., Bauer, J. K., & Semmens, J. B. (2014) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2013.12.003 · Cited by 137 · see citing works
“Nearly all privacy preserving protocols adopt an ‘honest-but-curious’ threat model [12], whereby parties are expected to try to carry out the protocol correctly, but will also try and find out as much information as they can from any data they receive.”
BackgroundA comprehensive review on privacy preserving data mining
Aldeen, Y. A. A. S., Salleh, M., & Razzaque, M. A. (2015) · SpringerPlus · doi:10.1186/s40064-015-1481-x · Cited by 132 · see citing works
“Vatsalan et al. (2013) reviewed the technique called ‘Privacy-Preserving Record Linkage’ (PPRL), which allowed the linkage of databases to organizations by protecting the privacy.”
MethodGUILD: GUidance for Information about Linking Data sets†
Gilbert, R. E., Lafferty, R., Hagger‐Johnson, G., Harron, K. L., Zhang, L., Smith, P. W. F., Dibben, C. J. L., & Goldstein, H. (2017) · Journal of Public Health · doi:10.1093/pubmed/fdx037 · Cited by 126 · see citing works
Data Resource Profile: MedicineInsight, an Australian national primary health care database
Busingye, D., Gianacas, C., Pollack, A., Chidwick, K., Merrifield, A., Norman, S., Mullin, B., Hayhurst, R., Blogg, S., Havard, A., et al. (2019) · International Journal of Epidemiology · doi:10.1093/ije/dyz147 · Cited by 127 · see citing works
Die Methoden der sozialwissenschaftlichen Datenerhebung im digitalen Zeitalter
Leitgöb, H., & Wolbring, T. (2021) · Schriftenreihe der ASI · doi:10.1007/978-3-658-34396-5_1 · Cited by 119 · see citing works
Beesley, L. J., Salvatore, M., Fritsche, L. G., Pandit, A., Rao, A., Brummett, C. M., Willer, C. J., Lisabeth, L. D., & Mukherjee, B. (2019) · Statistics in Medicine · doi:10.1002/sim.8445 · Cited by 106 · see citing works
(Almost) all of entity resolution
Binette, O., & Steorts, R. C. (2022) · Science Advances · doi:10.1126/sciadv.abi8021 · Cited by 107 · see citing works
Composite Bloom Filters for Secure Record Linkage
Durham, E., Kantarcıoğlu, M., Xue, Y., Toth, C., Kuzu, M., & Malin, B. (2013) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2013.91 · Cited by 86 · see citing works
Cryptanalysis of Basic Bloom Filters Used for Privacy Preserving Record Linkage
Niedermeyer, F., Steinmetzer, S., Kroll, M., & Schnell, R. (2014) · Journal of Privacy and Confidentiality · doi:10.29012/jpc.v6i2.640 · Cited by 82 · see citing works
Yigzaw, K. Y., Michalas, A., & Bellika, J. G. (2017) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-016-0389-x · Cited by 82 · see citing works
“Privacy-preserving record linkage (PPRL; also known as private set intersection and private record linkage) protocols have been developed to link records across multiple data custodians without revealing any information other than the linkage result [22, 23].”
BackgroundMethodInfluentialModern Privacy-Preserving Record Linkage Techniques: An Overview
Gkoulalas-Divanis, A., Vatsalan, D., Karapiperis, D., & Kantarcıoğlu, M. (2021) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2021.3114026 · Cited by 74 · see citing works
Self-citationKarapiperis, D., & Verykios, V. S. (2014) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2014.2349916 · Cited by 69 · see citing works
“Record Linkage consists of two steps.”
Self-citationSecurity and Privacy in Big Data Life Cycle: A Survey and Open Challenges
Koo, J., Kang, G., & Kim, Y. (2020) · Sustainability · doi:10.3390/su122410571 · Cited by 69 · see citing works
“[68] Privacy-Preserving Record Linkage Presented a survey of privacy-preserving record linkage technologies in the past and present.”
BackgroundPrivacy preserving interactive record linkage (PPIRL)
Kum, H., Krishnamurthy, A. K., Machanavajjhala, A., Reiter, M. K., & Ahalt, S. C. (2013) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2013-002165 · Cited by 64 · see citing works
Privacy-preserving matching of similar patients
Vatsalan, D., & Christen, P. (2015) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2015.12.004 · Cited by 63 · see citing works
“The overall similarity of each record with the query record is calculated as average 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 23…”
InfluentialSelf-citationThe transition from genomics to phenomics in personalized population health
Yurkovich, J. T., Evans, S. J., Rappaport, N., Boore, J. L., Lovejoy, J. C., Price, N. D., & Hood, L. (2023) · Nature Reviews Genetics · doi:10.1038/s41576-023-00674-x · Cited by 61 · see citing works
“Efforts to preserve the privacy of records while sharing them across organizations (known as privacy-preserving record linkage) are underway 60,61 but will require large-scale efforts and policy changes to become ubiquitous.”
Fold-stratified cross-validation for unbiased and privacy-preserving federated learning
Bey, R., Goussault, R., Grolleau, F., Benchoufi, M., & Porcher, R. (2020) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocaa096 · Cited by 60 · see citing works
“Some protocols have been developed and deployed to enable privacy-preserving deduplication in a federated learning setting, but they still require further research to be scalable [43–45].”
MethodEfficient Cryptanalysis of Bloom Filters for Privacy-Preserving Record Linkage
Christen, P., Schnell, R., Vatsalan, D., & Ranbaduge, T. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-57454-7_49 · Cited by 60 · see citing works
“…growing concerns about privacy and confidentiality increasingly preclude the exchange or sharing of personal identifying attributes, such as names, dates of birth, and addresses, which are generally required for linking databases due to the non-existence of common unique entity identifiers [4,18].”
BackgroundMethodInfluentialSelf-citationBozorgmehr, K., McKee, M., Muscat, N. A., Bartovic, J., Campos-Matos, I., Gerganova, T.-I., Hannigan, A., Janković, J., Kállayová, D., Kaplan, J., et al. (2023) · The Lancet Regional Health - Europe · doi:10.1016/j.lanepe.2023.100744 · Cited by 64 · see citing works
“85,86 It involves using encrypted IDs to link data, making it hard or impossible to identify speci fi c people.”
BackgroundPrecise and Fast Cryptanalysis for Bloom Filter Based Privacy-Preserving Record Linkage
Christen, P., Ranbaduge, T., Vatsalan, D., & Schnell, R. (2018) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2018.2874004 · Cited by 59 · see citing works
“Using record-level BFs will increase privacy, however it will likely lead to reduced linkage quality because only a single similarity can be calculated between two records (compared to one similarity per attribute when attribute-level BFs are used) [5].”
BackgroundMethodInfluentialSelf-citationA Clustering-Based Framework to Control Block Sizes for Entity Resolution
Fisher, J., Christen, P., Wang, Q., & Rahm, E. (2015) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/2783258.2783396 · Cited by 56 · see citing works
“…is being performed as a pre-processing step for an ER technique with scalability worse than quadratic, such as Markov logic networks [20], or privacy-preserving record linkage [22], then the time saving will be even greater than that indicated by the reduction in the number of candidate pairs.”
BackgroundInfluentialSelf-citationAn Evaluation Framework for Privacy-Preserving Record Linkage
Vatsalan, D., Christen, P., O’Keefe, C. M., & Verykios, V. S. (2014) · Journal of Privacy and Confidentiality · doi:10.29012/jpc.v6i1.636 · Cited by 55 · see citing works
Self-citationEcological and individualistic fallacies in health disparities research
Shih, Y. T., Bradley, C. J., & Yabroff, K. R. (2023) · JNCI Journal of the National Cancer Institute · doi:10.1093/jnci/djad047 · Cited by 52 · see citing works
Randomized Response and Balanced Bloom Filters for Privacy Preserving Record Linkage
Schnell, R., & Borgs, C. (2016) · IEEE ... International Conference on Data Mining workshops · doi:10.1109/icdmw.2016.0038 · Cited by 51 · see citing works
“Over the last decade, an increasing number of publications proposing novel PPRL methods have been published [12].”
BackgroundSocial Genome: Putting Big Data to Work for Population Informatics
Kum, H., Krishnamurthy, A. K., Machanavajjhala, A., & Ahalt, S. C. (2014) · Computer · doi:10.1109/mc.2013.405 · Cited by 49 · see citing works
“5 In practice, trusted third parties with access to all the data perform data linkage and integration.”
Stammler, S., Kussel, T., Schoppmann, P., Stampe, F., Tremper, G., Katzenbeisser, S., Hamacher, K., & Lablans, M. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btaa764 · Cited by 49 · see citing works
Schmidlin, K., Clough‐Gorr, K. M., Spoerri, A., & for the SNC study group (2015) · BMC Medical Research Methodology · doi:10.1186/s12874-015-0038-6 · Cited by 49 · see citing works
“into two-party and three-party protocols [44, 47, 48].”
MethodO'Brien, E. C., Rodríguez, A. M., Kum, H., Schanberg, L. E., Fitz-Randolph, M., O’Brien, S. M., & Setoguchi, S. (2019) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2019.04.003 · Cited by 48 · see citing works
Scalable Privacy-Preserving Record Linkage for Multiple Databases
Vatsalan, D., & Christen, P. (2014) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/2661829.2661875 · Cited by 46 · see citing works
“To assess the privacy of our protocol, we assume all parties follow the honest-but-curious adversary model [13], in that they are curious and try to find out as much as possible about the other parties’ data while following the protocol.”
BackgroundInfluentialSelf-citationBaraliakos, X., Richter, A., Feldmann, D., Ott, A., Buelow, R., Schmidt, C. O., & Braun, J. (2020) · Annals of the Rheumatic Diseases · doi:10.1136/annrheumdis-2020-218669 · Cited by 42 · see citing works
FEDERAL: A Framework for Distance-Aware Privacy-Preserving Record Linkage
Karapiperis, D., Gkoulalas-Divanis, A., & Verykios, V. S. (2017) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2017.2761759 · Cited by 41 · see citing works
“Although this is a wellestablished and very popular model [1], [2], [3], [4], [10] for performing PPRL, in the following, we provide additional safeguards under certain models of attack.”
BackgroundSelf-citationEze, B., & Peyton, L. (2015) · Procedia Computer Science · doi:10.1016/j.procs.2015.08.353 · Cited by 39 · see citing works
A novel ensemble learning approach to unsupervised record linkage
Jurek, A., Hong, J. S., Chi, Y., & Liu, W. (2017) · Information Systems · doi:10.1016/j.is.2017.06.006 · Cited by 37 · see citing works
“In particular for privacy preserving RL, where the data is private and confidential [10], it may be impossible to label the data manually.”
BackgroundAsher, J., Resnick, D. M., Brite, J., Brackbill, R. M., & Cone, J. E. (2020) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph17186937 · Cited by 37 · see citing works
“The next step in PPRL is some type of encoding of private information using a secure coding scheme.”
BackgroundInfluentialEfficient two-party private blocking based on sorted nearest neighborhood clustering
Vatsalan, D., Christen, P., & Verykios, V. S. (2013) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/2505515.2505757 · Cited by 35 · see citing works
“Two different types of privacy techniques have been used to address the privacy aspect in PPRL [32].”
BackgroundMethodInfluentialSelf-citationIssues and Challenges in Census Taking
Skinner, C. J. (2017) · Annual Review of Statistics and Its Application · doi:10.1146/annurev-statistics-041715-033713 · Cited by 35 · see citing works
Probabilistic Prediction of Privacy Risks in User Search Histories
Biega, J., Mele, I., & Weikum, G. (2014) · doi:10.1145/2663715.2669609 · Cited by 34 · see citing works
“, [12, 16, 31]).”
Application of Privacy-Preserving Techniques in Operational Record Linkage Centres
Boyd, J., Randall, S. M., & Ferrante, A. M. (2015) · Springer eBooks · doi:10.1007/978-3-319-23633-9_11 · Cited by 34 · see citing works
A Graph Matching Attack on Privacy-Preserving Record Linkage
Vidanage, A., Christen, P., Ranbaduge, T., & Schnell, R. (2020) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3340531.3411931 · Cited by 34 · see citing works
“PPRL methods can be divided into two categories, perturbation and secure multi-party computation (SMC) based techniques [35].”
BackgroundMethodInfluentialSelf-citationNóbrega, T., Pires, C. E. S., & Nascimento, D. C. (2021) · Information Systems · doi:10.1016/j.is.2021.101826 · Cited by 33 · see citing works
Mirel, L. B., Resnick, D. M., Aram, J., & Cox, C. S. (2022) · Statistical Journal of the IAOS · doi:10.3233/sji-210891 · Cited by 32 · see citing works
“Privacy preserving record linkage or “PPRL” is a method that can be used to link de-identified data [6].”
A fast and efficient Hamming LSH-based scheme for accurate linkage
Karapiperis, D., & Verykios, V. S. (2016) · Knowledge and Information Systems · doi:10.1007/s10115-016-0919-y · Cited by 31 · see citing works
Self-citationKumar, M., & Singh, A. (2022) · Journal of Ambient Intelligence and Smart Environments · doi:10.3233/ais-220101 · Cited by 32 · see citing works
Brown, A., Borgs, C., Randall, S. M., & Schnell, R. (2017) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-017-0478-5 · Cited by 30 · see citing works
“A summary of privacy-preserving record linkage techniques notes that each method differs in its accuracy, maturity, practicality and suitability for large-scale linkages [14].”
BackgroundEstimating parameters for probabilistic linkage of privacy-preserved datasets
Brown, A., Randall, S. M., Ferrante, A. M., Semmens, J. B., & Boyd, J. (2017) · BMC Medical Research Methodology · doi:10.1186/s12874-017-0370-0 · Cited by 29 · see citing works
“Under a trusted third party linkage model [1], this operation occurs before the release of any data to record linkage units.”
BackgroundRichter, A., Sierocinski, E., Singer, S., Bülow, R., Hackmann, C., Chenot, J., & Schmidt, C. O. (2020) · European Journal of Epidemiology · doi:10.1007/s10654-020-00679-4 · Cited by 26 · see citing works
Taxonomy of Attacks on Privacy-Preserving Record Linkage
Vidanage, A., Ranbaduge, T., Christen, P., & Schnell, R. (2022) · Journal of Privacy and Confidentiality · doi:10.29012/jpc.764 · Cited by 26 · see citing works
Self-citationStatistical Data Privacy: A Song of Privacy and Utility
Slavković, A., & Seeman, J. (2022) · Annual Review of Statistics and Its Application · doi:10.1146/annurev-statistics-033121-112921 · Cited by 29 · see citing works
Scalable Privacy-Preserving Linking of Multiple Databases Using Counting Bloom Filters
Vatsalan, D., Christen, P., & Rahm, E. (2016) · IEEE ... International Conference on Data Mining workshops · doi:10.1109/icdmw.2016.0130 · Cited by 25 · see citing works
“masked records sent to it by the database owners [3].”
BackgroundMethodInfluentialSelf-citationDistance-Aware Encoding of Numerical Values for Privacy-Preserving Record Linkage
Karapiperis, D., Gkoulalas-Divanis, A., & Verykios, V. S. (2017) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde.2017.58 · Cited by 25 · see citing works
“The anonymization of numerical values is of paramount importance in PPRL processes that operate under the threeparty model [5].”
BackgroundMethodSelf-citationAn iterative two-party protocol for scalable privacy-preserving record linkage
Vatsalan, D., & Christen, P. (2012) · ANU Open Research (Australian National University) · Cited by 23 · see citing works
Self-citationBiega, J., Gummadi, K. P., Mele, I., Milchevski, D., Tryfonopoulos, C., & Weikum, G. (2016) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/2911451.2911533 · Cited by 22 · see citing works
Brown, A., Ferrante, A. M., Randall, S. M., Boyd, J., & Semmens, J. B. (2017) · Frontiers in Public Health · doi:10.3389/fpubh.2017.00034 · Cited by 23 · see citing works
Privacy Preserving Record Linkage with PPJoin.
Sehili, Z., Kolb, L., Borgs, C., Schnell, R., & Rahm, E. (2015) · Cited by 22 · see citing works
Privacy-Preserving Deep Learning Based Record Linkage
Ranbaduge, T., Vatsalan, D., & Ding, M. (2023) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2023.3342757 · Cited by 22 · see citing works
“A popular approach to link sensitive data in a privacypreserving way is to encode quasi-identifying values, such as using Bloom filters, that allows fuzzy matching by calculating approximate similarities on the encoded values in order to identify matches and non-matches [1], [2], [4].”
BackgroundMethodInfluentialSelf-citationPrivacy, Security, and Legal Issues in the Health Cloud: Structured Review for Taxonomy Development
Zandesh, Z. (2024) · JMIR Formative Research · doi:10.2196/38372 · Cited by 21 · see citing works
“Vatsalan et al [32] Compliance, Data protection, Identity cre-dential access management, Ownership, and Quality of service This work was a systematic review that introduced a legal framework for the health cloud with 5 main pillars and 17 subcomponents, and defined the role of legal aspects in the…”
BackgroundA blinded evaluation of privacy preserving record linkage with Bloom filters
Randall, S. M., Wichmann, H., Brown, A., Boyd, J., Eitelhuber, T., Merchant, A., & Ferrante, A. M. (2022) · BMC Medical Research Methodology · doi:10.1186/s12874-022-01510-2 · Cited by 23 · see citing works
“Growing concerns about individual data privacy, along with an increasing demand for linked data from researchers has resulted in a burgeoning literature of new algorithmic approaches for providing PPRL [1].”
BackgroundScalable Blocking for Privacy Preserving Record Linkage
Karakasidis, A., Koloniari, G., & Verykios, V. S. (2015) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/2783258.2783290 · Cited by 21 · see citing works
“A survey classifying privacy preserving blocking and matching methods along fifteen dimensions can be found in [22].”
BackgroundMethodInfluentialSelf-citationThe Past, Present and Future of the German Record Linkage Center (GRLC)
Antoni, M., & Schnell, R. (2017) · Jahrbücher für Nationalökonomie und Statistik · doi:10.1515/jbnst-2017-1004 · Cited by 21 · see citing works
An efficient privacy-preserving record linkage technique for administrative data and censuses
Schnell, R. (2014) · Statistical Journal of the IAOS · doi:10.3233/sji-140833 · Cited by 20 · see citing works
“This class of methods is called “privacy preserving record linkage techniques” (for a review, see [23]).”
MethodResultSecure Approximate String Matching for Privacy-Preserving Record Linkage
Essex, A. (2019) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2019.2903651 · Cited by 20 · see citing works
“Public-key approaches in general, however, are often regarded as too computationally intensive [4], [10], [11], [21], and much of the literature has chosen to trade security guarantees in favor of efficiency.”
BackgroundMethodClustering-Based Scalable Indexing for Multi-party Privacy-Preserving Record Linkage
Ranbaduge, T., Vatsalan, D., & Christen, P. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-18032-8_43 · Cited by 19 · see citing works
“Some of these have been adapted for PPRL [16], including standard blocking [1], mapping based indexing [8], clustering [8,10,19], and locality sensitive hashing [7].”
BackgroundMethodSelf-citationSorted Nearest Neighborhood Clustering for Efficient Private Blocking
Vatsalan, D., & Christen, P. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-37456-2_29 · Cited by 19 · see citing works
“However, as with other three-party protocols, collusion between the third party and one of the database owners with the aim to identify the other database owner’s data, is a privacy risk in this approach as well [4, 5].”
BackgroundSelf-citationParallel Privacy-preserving Record Linkage using LSH-based Blocking
Franke, M., Sehili, Z., & Rahm, E. (2018) · International Conference on Internet of Things, Big Data and Security (IoTBDS) · doi:10.5220/0006682701950203 · Cited by 18 · see citing works
“Similar as in former work, at first each party individually conducts a preprocessing step where records are encoded and static BKs can be defined (Vatsalan et al., 2013).”
BackgroundMethodResultInfluentialHashing-Based Distributed Multi-party Blocking for Privacy-Preserving Record Linkage
Ranbaduge, T., Vatsalan, D., Christen, P., & Verykios, V. S. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-31750-2_33 · Cited by 18 · see citing works
Self-citationIncremental clustering techniques for multi-party Privacy-Preserving Record Linkage
Vatsalan, D., Christen, P., & Rahm, E. (2020) · Data & Knowledge Engineering · doi:10.1016/j.datak.2020.101809 · Cited by 18 · see citing works
“LU-based approaches for PPRL are well suited for efficient linking of multiple large databases for practical applications, as the number of communication steps required among the database owners, as well as the risk of information leakage from a sensitive database to other database owners, are reduced when a LU is use…”
BackgroundMethodInfluentialSelf-citationSandnes, F. E. (2021) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3411763.3451794 · Cited by 19 · see citing works
“domain, among researchers working on register data/microdata, and more recently among data scientists [16, 49, 52].”
BackgroundDoes Big Data Change the Privacy Landscape? A Review of the Issues
Keller‐McNulty, S., Shipp, S. S., & Schroeder, A. D. (2016) · Annual Review of Statistics and Its Application · doi:10.1146/annurev-statistics-041715-033453 · Cited by 18 · see citing works
A distributed near-optimal LSH-based framework for privacy-preserving record linkage
Karapiperis, D., & Verykios, V. S. (2014) · Computer Science and Information Systems · doi:10.2298/csis140215040k · Cited by 18 · see citing works
“We refer the reader to [44] where various classification techniques are discussed.”
BackgroundMethodInfluentialSelf-citationA Survey of Blocking and Filtering Techniques for Entity ResolutionPreprint
Papadakis, G., Skoutas, D., Thanos, E., & Palpanas, T. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1905.06167 · Cited by 18 · see citing works
“In contrast, Blocking constitutes an integral part of privacy-preserving ER, with several relevant works (for details, refer to a recent survey [148]).”
BackgroundRescriber: Smaller-LLM-Powered User-Led Data Minimization for LLM-Based Chatbots
Zhou, J., Xu, E., Wu, Y., & Li, T. (2025) · Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713701 · Cited by 17 · see citing works
“” Concerns about identifiability also extended beyond direct identifiers to demographic details like gender, raising fears of linkage attacks [50] and broader profiles emerging from multiple data points.”
Classification-Based Record Linkage With Pseudonymized Data for Epidemiological Cancer Registries
Siegert, Y., Jiang, X., Krieg, V., & Bartholomäus, S. (2016) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2016.2598482 · Cited by 17 · see citing works
Balancing Privacy and Information Disclosure in Interactive Record Linkage with Visual Masking
Ragan, E. D., Kum, H., Ilangovan, G., & Wang, H. (2018) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3173574.3173900 · Cited by 17 · see citing works
“Existing research on privacy-preserving record linkage is based on the application of cryptology to link data securely given a predetermined linkage function, which is unknown in most real situations [19, 34].”
BackgroundBig Data: Some Ethical Concerns for the Social Sciences
Weinhardt, M. (2021) · Social Sciences · doi:10.3390/socsci10020036 · Cited by 20 · see citing works
“…touched upon here, such as the question of proper risk assessment, proper procedures to protect privacy (e.g., privacy-preserving record linkage, Vatsalan et al. 2013), data sharing and archiving (e.g., Zimmer 2010; Borgman 2012; Bishop and Gray 2017), and the issue of data ownership (Politou…”
BackgroundBoenisch, F., Battis, V., Buchmann, N., & Poikela, M. E. (2021) · Mensch und Computer (MuC) · doi:10.1145/3473856.3473869 · Cited by 17 · see citing works
“…at Inference Time [7]), Cluster 2 (G: Differential Privacy [27], H: Homomorphic Encryption [37], I: Watermarking [58], J: Privacy Preserving Record Linkage [87]), Cluster 3 (K: Smoothing Prediction Output [35], L: Introducing Delay [77], M: Adversarial Training [36], N: Federated Learning [48]).”
BackgroundMethodPrivBV: Distance-aware encoding for distributed data with local differential privacy
Sun, L., Ping, G., & Ye, X. (2021) · Tsinghua Science & Technology · doi:10.26599/tst.2021.9010027 · Cited by 17 · see citing works
A distributed framework for scaling Up LSH-based computations in privacy preserving record linkage
Karapiperis, D., & Verykios, V. S. (2013) · Balkan Conference in Informatics · doi:10.1145/2490257.2490258 · Cited by 17 · see citing works
“An overview of privacy preserving blocking techniques is provided in [10].”
MethodSelf-citationPrivacy preserving record linkage for public health action: opportunities and challenges
Pathak, A., Serrer, L., Zapata, D., King, R. J., Mirel, L. B., Sukalac, T., Srinivasan, A., Baier, P., Bhalla, M. I., David-Ferdon, C. F., et al. (2024) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocae196 · Cited by 16 · see citing works
Efficient protocols for private record linkage
Wen, Z. A., & Dong, C. (2014) · ACM Symposium on Applied Computing · doi:10.1145/2554850.2555001 · Cited by 16 · see citing works
“A good survey of PRL protocols can be found in [23].”
BackgroundPrivacy Preserving Probabilistic Record Linkage Without Trusted Third Party
Lazrig, I., Ong, T. C., Ray, I., Ray, I., Jiang, X., & Vaidya, J. S. (2018) · Conference on Privacy, Security and Trust · doi:10.1109/pst.2018.8514192 · Cited by 16 · see citing works
“Privacy-preserving record linkage (PPRL) is a class of protocols that use encryption and specialized linkage algorithms that do not reveal identities to link records [2].”
BackgroundChallenges for privacy preservation in data integration
Christen, P., Vatsalan, D., & Verykios, V. S. (2014) · Journal of Data and Information Quality · doi:10.1145/2629604 · Cited by 15 · see citing works
Self-citationLim, D., Randall, S. M., Robinson, S., Thomas, E., Williamson, J., Chakera, A., Napier, K. R., Schwan, C., Manuel, J., Betts, K. S., et al. (2022) · Applied Clinical Informatics · doi:10.1055/s-0042-1757174 · Cited by 15 · see citing works
Large-Scale Multi-party Counting Set Intersection Using a Space Efficient Global Synopsis
Karapiperis, D., Vatsalan, D., Verykios, V. S., & Christen, P. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-18123-3_20 · Cited by 15 · see citing works
“This has led to an active research area, known as PPSI, in the field of privacypreserving data mining [3] and specifically in private data matching [19,35].”
BackgroundMethodSelf-citationGood Practice Data Linkage (GPD): A Translation of the German Version
March, S., Andrich, S., Drepper, J., Horenkamp‐Sonntag, D., Icks, A., Ihle, P., Kieschke, J., Kollhorst, B., Maier, B., Meyer, I., et al. (2020) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph17217852 · Cited by 15 · see citing works
Optimization of the mainzelliste software for fast privacy-preserving record linkage
Rohde, F., Franke, M., Sehili, Z., Lablans, M., & Rahm, E. (2021) · Journal of Translational Medicine · doi:10.1186/s12967-020-02678-1 · Cited by 14 · see citing works
Rechtliche Rahmenbedingungen der Organisationsdatenforschung
Grenzer, M., Meyer, I., Schuster, H., & Gebel, T. (2017) · Springer eBooks · doi:10.1007/978-3-658-08493-6_8 · Cited by 13 · see citing works
Kussel, T., Brenner, T., Tremper, G., Schepers, J., Lablans, M., & Hamacher, K. (2022) · Journal of Translational Medicine · doi:10.1186/s12967-022-03671-6 · Cited by 13 · see citing works
“…record linkage implementations (e.g., [25, 34]) are mostly based on Privacy-Preserving Record Linkage (PPRL) techniques using pre-processed (e.g., hashed) identifying data such as first and surname, birth date, address, etc. that is linked centrally using a Third Trusted Party (TTP) [50].”
MethodA Vulnerability Assessment Framework for Privacy-preserving Record Linkage
Vidanage, A., Christen, P., Ranbaduge, T., & Schnell, R. (2023) · ACM Transactions on Privacy and Security · doi:10.1145/3589641 · Cited by 13 · see citing works
“The techniques proposed for PPRL can be divided into two major categories: perturbation- and secure multi-party computation (SMC)-based techniques [65].”
BackgroundMethodInfluentialSelf-citationA systematic review on privacy-preserving distributed data mining
Sun, C., Ippel, L., Dekker, A. L. A. J., Dumontier, M., & van Soest, J. (2021) · Data Science · doi:10.3233/ds-210036 · Cited by 12 · see citing works
“Some efficient and privacy-compliant algorithms for the field of privacy-preserving entity matching have been developed [16,41,47,111] in the past 10 years.”
MethodHuman rights as a topic and guide for LIS research and practice
Mathiesen, K. (2015) · Journal of the Association for Information Science and Technology · doi:10.1002/asi.23293 · Cited by 13 · see citing works
Privacy preserving record linkage in the presence of missing values
Chi, Y., Hong, J. S., Jurek, A., Liu, W., & O’Reilly, D. (2017) · Information Systems · doi:10.1016/j.is.2017.07.001 · Cited by 13 · see citing works
“Introduction Record linkage, also known as data matching, duplicate detection, or entity resolution, refers to the process of identifying and aggregating records from one or more datasets, which represent the same real-world entities [1, 2].”
BackgroundMethodInfluential
- Authors
- Vatsalan, D., Christen, P., & Verykios, V. S.
- Affiliations
- Australian National University (AU)
- Hellenic Open University (GR)
- Corresponding author
- Vatsalan, Dinusha
- Venue
- Information Systems
- Publisher
- Elsevier BV
- Published
- 2013-09
- Volume
- 38
- Issue
- 6
- Pages
- 946-969
- ISSN
- 0306-4379 (ISSN-L)
- OpenAlex
- W2029128277
- Cited by
- 295
- References
- 147
- Citations by year
- 2026: 20 · 2025: 14 · 2024: 14 · 2023: 17 · 2022: 23 · 2021: 28 · 2020: 31 · 2019: 22 · 2018: 21 · 2017: 35 · 2016: 20 · 2015: 23 · 2014: 16 · 2013: 10 · 2012: 1
- FWCI
- 21.21
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Information Systems (publishedVersion)
- ANU Open Research (Australian National University) (submittedVersion)
- Topics
- Social Sciences › Decision Sciences › Management Science and Operations Research › Data Quality and Management
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Social Sciences › Sociology and Political Science › Privacy, Security, and Data Protection
- Keywords
- privacy-preserving record linkage, data linkage, entity resolution, data matching, personally identifiable information, data privacy, data integration, data quality
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