Privacy-Preserving Biometric Identification Using Secure Multiparty Computation: An Overview and Recent Trends
By Julien Bringer, Hervé Chabanne, Alain Patey. 2013
134 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Electrical and Electronic Engineering, Signal Processing
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Praneeth Vepakomma
- Ramesh Raskar
- Abhishek Singh
- Otkrist Gupta
- Michael Zohner
- Thomas Schneider
- Julien Bringer
- Hervé Chabanne
In Electrical and Electronic Engineering
- Thomas Schneider
- Michael Zohner
- Otkrist Gupta
- Ramesh Raskar
- Abhishek Singh
- Praneeth Vepakomma
- Marcel Keller
- Adrià Gascón
In Signal Processing
- Thomas Schneider
- Julien Bringer
- Ramesh Raskar
- Abhishek Singh
- Hervé Chabanne
- Praneeth Vepakomma
- Alain Patey
Citations in the corpus
- NoPeek: Information leakage reduction to share activations in distributed deep learning (2020, background): “The work in [39, 40, 41] describes HE and garbled circuits (an MPC scheme) for privacy-preserving biometric identification.”
- GSHADE: faster privacy-preserving distance computation and biometric identification (2014, background): “More details on S2PC can for instance be found in [24], while a deeper study of its application to biometric identification can be found in [8].”