Privacy-preserving entity resolution and logistic regression on encrypted data

By Mentari Djatmiko, Guillaume Smith, Brian Thorne, Dongyao Wu. 2017

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Stephen Hardy
2. Richard Nock
3. Giorgio Patrini
4. Wilko Henecka
5. Runhua Xu
6. Hamish Ivey-Law
7. Heiko Ludwig
8. Nathalie Baracaldo

Citations in the corpus

• Entity Resolution and Federated Learning get a Federated Resolution (2018, methodology): “1 Introduction With the ever-expanding collection of data, it is becoming common practice for organisations to cooperate with the objective of leveraging their joint collections of data [12, 13], with a wider The Australian National University & the University of Sydney Now with the University of Am”
• HybridAlpha: An Efficient Approach for Privacy-Preserving Federated Learning (2019, methodology): “To tackle the issues and challenges in the case of vertically partitioned data, several methods are proposed in [9, 14, 22], which focus on entity resolution and simple machine learning models, like logistic regression.”