Privacy preserving schema and data matching

By M. Scannapieco, Ilya Figotin, Elisa Bertino, Ahmed K. Elmagarmid. 2007

217 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications, Management Science and Operations Research

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In Artificial Intelligence

In Computer Networks and Communications

In Management Science and Operations Research

Citations in the corpus

Federated Machine Learning (2019, methodology): “Since the user groups of the two companies are not the same, the system uses the encryption-based user ID alignment techniques such as those described in [38, 56] to confirm the common users of both parties without A and B exposing their respective data.”
Mining Frequent Patterns with Differential Privacy (2013, methodology): “We compared our solution with the state of the art secure transformation technique [19], and our proposed approach provides better scalability and stronger privacy while achieves comparable utility results.”
A taxonomy of privacy-preserving record linkage techniques (2013, methodology): “The attribute values are embedded (mapped) into a metric space [75,102] while the distances between these values are preserved.”
Efficient two-party private blocking based on sorted nearest neighborhood clustering (2013, background): “…proposed recently that adopt existing techniques (such as standard blocking [1, 14, 15, 18], sorted neighborhood blocking or windowing [19, 30], mapping based blocking [25, 33], q -gram based blocking [3], clustering [17, 21], and locality sensitive hashing [10]) into a privacy-preserving context.”
Data Matching (2012, background): “[234] addressed both the problem of private schema and private data matching.”
A Comprehensive Survey of Privacy-preserving Federated Learning (2021, methodology): “• In the FL, the intermediate weights or gradients communicated between participants and the aggregator may reveal sensitive information about the participants’ training datasets, as shown in References [96, 137, 198, 212].”
Secure multiple linear regression based on homomorphic encryption (2011, background): “Obtaining such identifiers when they do not naturally exist (e.g., social security numbers or ID numbers) is the problem of record linkage, and is given a secure treatment in e.g., Scannapieco [33].”
Privacy-Preserving Record Linkage (2010, methodology): “Another secure vector space method to compute edit distances is described by [33].”