Privacy-Preserving Deep Models for Plant Stress Phenotyping

Minsu Cho, Koushik Nagasubramanian, Asheesh K Singh, Arti Singh, Baskar Ganapathysubramanian, Soumik Sarkar, Chinmay Hegde

Keywords: deep networks, data privacy, security, multiparty communication

TL;DR: We design neural networks for plant stress phenotyping that support efficient secure inference.

Abstract: Deep neural networks are increasing being deployed for automating plant stress identification and quantification. However, as this area grows in importance, alleviating privacy concerns of practitioners becomes a major challenge. In this paper, we present a deep learning framework for plant stress phenotyping that guarantees privacy to both the owner of the data, as well as the developer of the model. Our framework leverages recent advances in deep neural network design for accelerated private inference (PI) using secure multiparty communication. We showcase our framework on a large-scale image dataset, demonstrate that our newly de-signed models enjoy nearly 2 orders of magnitude speedup in inference time over state-of-the-art baselines.