Efficient Evaluation of Activation Functions over Encrypted Data

Efficient Evaluation of Activation Functions over Encrypted Data
Patricia Thaine (University of Toronto)

Presented at the
2nd Deep Learning and Security Workshop
May 23, 2019
at the 2019 IEEE Symposium on Security & Privacy
San Francisco, CA
https://www.ieee-security.org/TC/SP2019/
https://www.ieee-security.org/TC/SPW2019/DLS/

ABSTRACT
We describe a method for approximating any bounded activation function given encrypted input data. The utility of our method is exemplified by simulating it within two typical machine learning tasks: namely, a Variational Autoencoder that learns a latent representation of MNIST data, and an MNIST image classifier.