EPIC: Efficient Private Image Classification (or: Learning from the Masters)

By Eleftheria Makri, Dragos Rotaru, Nigel P. Smart, Frederik Vercauteren. 2019

48 citations recorded by the source corpus.

Subfields: Artificial Intelligence

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

1. Marcel Keller
2. Daniel E. Escudero
3. Peter Scholl
4. Praneeth Vepakomma
5. Abhishek Singh
6. Ramesh Raskar
7. Nikolaj Volgushev
8. Tore Kasper Frederiksen

Citations in the corpus

• Privacy in Deep Learning: A Survey (2020, methodology): “Most of the efforts in the field of SMC for deep learning are focused on speeding up the computation, as demonstrated above, and also by [102], [103], [113].”
• Falcon: Honest-Majority Maliciously Secure Framework for Private Deep Learning (2020, methodology): “EPIC [41] demonstrates the use of transfer learning in the space of privacy-preserving machine learning while Quotient [42] takes the first steps in developing two party secure computation protocols for optimizers and normalizations.”
• New Primitives for Actively-Secure MPC over Rings with Applications to Private Machine Learning (2019, methodology): “This of course causes loss in accuracy, however, this rarely causes a problem and for real data the constant does not necessarily have to be large to avoid losing classification accuracy [29].”