1 research outputs found
Outsourcing Private Machine Learning via Lightweight Secure Arithmetic Computation
In several settings of practical interest, two parties seek to
collaboratively perform inference on their private data using a public machine
learning model. For instance, several hospitals might wish to share patient
medical records for enhanced diagnostics and disease prediction, but may not be
able to share data in the clear because of privacy concerns. In this work, we
propose an actively secure protocol for outsourcing secure and private machine
learning computations. Recent works on the problem have mainly focused on
passively secure protocols, whose security holds against passive
(`semi-honest') parties but may completely break down in the presence of active
(`malicious') parties who can deviate from the protocol. Secure neural networks
based classification algorithms can be seen as an instantiation of an
arithmetic computation over integers.
We showcase the efficiency of our protocol by applying it to real-world
instances of arithmetized neural network computations, including a network
trained to perform collaborative disease prediction