GSHADE: faster privacy-preserving distance computation and biometric identification
By Julien Bringer, Hervé Chabanne, M. Favre, Alain Patey, Thomas Schneider, Michael Zohner. 2014
98 citations recorded by the source corpus.
Subfields: Computer Vision and Pattern Recognition, Genetics, 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 Computer Vision and Pattern Recognition
- Ramesh Raskar
- Mohammad Haghighat
- Mohamed Abdel-Mottaleb
- Saman A. Zonouz
- Otkrist Gupta
- Praneeth Vepakomma
- Arpita Patra
- Ajith Suresh
In Genetics
In Signal Processing
- Mohammad Haghighat
- Mohamed Abdel-Mottaleb
- Saman A. Zonouz
- Hossein Yalame
- Arpita Patra
- Ajith Suresh
- Thomas Schneider
- Ramesh Raskar
Citations in the corpus
- NoPeek: Information leakage reduction to share activations in distributed deep learning (2020, methodology): “Similarly [42, 43] uses MPC schemes like oblivious transfer, secure millionaire, secure dot product for face matching with respect to a collection of sensitive surveillance footage images.”
- CloudID: Trustworthy cloud-based and cross-enterprise biometric identification (2015, methodology): “The algorithms proposed in Bringer et al. (2009, 2011, 2013a, 2013b, 2014) do not guarantee zero data disclosure and suffer from information leakage.”
- ABY2.0: Improved Mixed-Protocol Secure Two-Party Computation (2020, methodology): “Mixed 2PC protocols that combine GC-based and SS-based approaches benefit from their respective advantages and were used in many privacypreserving applications such as face recognition [49], fingerprint recognition [24], biometric matching [39], and machine learning [57, 73, 75, 91].”