Data Matching

By Peter Christen. 2012

820 citations recorded by the source corpus.

Subfields: Management Science and Operations Research, Algorithms & Complexity

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Citations in the corpus

Advances and Open Problems in Federated Learning (2019, background): “For example, if data is vertically partitioned, each party maintaining private records of different feature sets about common entities, these problems may require addressing record linkage [108] within the federated learning task.”
Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption (2017, methodology): “An approximate solution is given by techniques for entity resolution [Christen, 2012]; Figure 2 gives a pictorial representation.”
Fast Learning from Distributed Datasets without Entity Matching (2016, background): “Entity resolution (ER), or entity matching [Christen, 2012], would be the traProceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence (IJCAI-16)”
Entity Resolution and Federated Learning get a Federated Resolution (2018, methodology): “In addition to learning a classifier, federated learning thus faces the mandatory preprocessing step of matching rows in the datasets of A and B to build dataset Ŝ, a preprocessing step we define as entity resolution [8].”
Toward a System Building Agenda for Data Integration (and Data Science) (2018, background): “Over the past few decades, DI has received much attention (e.g., [37, 29, 31, 20, 34, 33, 6, 17, 39, 22, 23, 5, 8, 36, 15, 35, 4, 25, 38, 26, 32, 19, 2, 12, 11, 16, 2, 3]).”
A Comprehensive Study of the Past, Present, and Future of Data Deduplication (2016, neutral): “In these fields, duplicate detection is commonly used to identify records that represent the same entity [33, 34].”
A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection (2019, methodology): “For the vertical FLS, it usually adopts entity alignment techniques [39], [190] to collect the overlapped samples of the parties.”
Privacy-preserving entity resolution and logistic regression on encrypted data (2017, background): “Privacy-preserving entity resolution When the dataset is vertically partitioned across multiple organisations the problem arises of how to identify the corresponding entities, namely entity resolution (Christen, 2012).”