Privacy-Preserving Record Linkage

By Rob Hall, Stephen E. Fienberg. 2010

147 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Management Science and Operations Research

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Citations in the corpus

Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption (2017, neutral): “tching a prerequisite for working with vertically partitioned in most realistic scenarios. Finding efﬁcient and privacy-compliant algorithms is a ﬁeld in itself, privacy-preserving entity resolution [Hall and Fienberg, 2010, Christen, 2012, Vatsalan et al., 2013a]. Table 1 summarizes some comparison”
A Novel Error-Tolerant Anonymous Linking Code (2011, background): “Approaches of the second type are commonly labelled “privacy-preserving record linkage” in the literature [7,8].”
A taxonomy of privacy-preserving record linkage techniques (2013, background): “PPRL techniques generally consider one of the two adversary models that are commonly used in the ﬁeld of cryptography, and especially in the area of secure multi-party computation (SMC) [70,82,83].”
Efficient two-party private blocking based on sorted nearest neighborhood clustering (2013, background): “We assume that both Alice and Bob follow the semi-honest (honest-but-curious) adversary model [12, 32], in that they follow the protocol while trying to ﬁnd out as much as possible about the data from the other party.”
Data Matching (2012, background): “In the second security model, known as the “malicious” model [123, 134], it is assumed that the parties that take part in a protocol do not necessarily follow the steps of the protocol, but they might maliciously communicate either wrong or modified data in order to be able to infer any sensitive in”