Combining Secret Sharing and Garbled Circuits for Efficient Private IEEE 754 Floating-Point Computations

By Pille Pullonen, Sander Siim. 2015

54 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems

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. Adrià Gascón
2. Farinaz Koushanfar
3. David E. Evans
4. M. Sadegh Riazi
5. Thomas Schneider
6. Mariana Raykova
7. Ebrahim M. Songhori
8. Samee Zahur

In Information Systems

1. David E. Evans
2. Samee Zahur
3. Farinaz Koushanfar
4. M. Sadegh Riazi
5. Thomas Schneider
6. Adrià Gascón
7. Mariana Raykova
8. Borja Balle

Citations in the corpus

• Privacy-Preserving Distributed Linear Regression on High-Dimensional Data (2017, methodology): “However, efficient implementations of IEEE 754compliant secure MPC protocols are a subject of on-going research [17, 61], with the current state-of-the-art yielding 32 bit floating-point multiplication circuits running in time comparable to computers from the 1960’s [68].”
• Chameleon: A Hybrid Secure Computation Framework for Machine Learning Applications (2018, methodology): “The quality of the algorithms varies from self-made to properly implemented IEEE 754 algorithms, such as in [32, 73].”
• Oblivious Neural Network Predictions via MiniONN Transformations (2017, background): “For example, in the sigmoid function f (y) = 1 1+e−y , both e y and division are expensive to be computed by 2PC protocols [49].”