Neural Information Processing Systems 2021
(NeurIPS 2021) Demos
MNIST and CIFAR-10
- Network A — 3 fully connected layer with ReLU
- Network B — 1 Convolutional layer, 2 fully connected layers with ReLU
- Network C — 2 Convolutional layers, 1 fully connected layer with ReLU
Compare total execution time to current state-of-the-art protocols, including SecureNN, Gazelle, MiniONN, Chameleon.
See the figure below, times in seconds.
Comparison of the supported layers and functions
Interactive Demo Plan
We’ll discuss the motivation behind TripleBlind and what it means to “unlock private data sharing.”
We’ll cover the underlying methodology of our innovations, including Blind Learning and Privophy.
We’ll demo the use of TripleBlind with a live example where we train an image classifier using two remote decentralized datasets.
We’ll invite the audience to play with our solution.
Audience Interaction Plan
Members of the audience will be invited to interact with our system. Each participant will play the role of either a data scientist or a data owner.
The data scientist task will focus on training a deep learning model using remote decentralized datasets.
The data owner task will focus on running an encrypted inference using a remote model on some local data.
Accessible through live Jupyter Notebooks
Each notebook will include one of five scenarios (see below). Note that each dataset is divided into two sets, each set is placed on an individual Google Cloud instance to simulate training and inference on decentralized data owned by different organizations.
Training a deep learning model (VGG-16) for image classification using CIFAR-10
Training a deep learning model for tabular data classification
Training a deep learning model for multi-modal (text and images) classification
Running a secure inference task using a remote, pre-trained model
We have also added another notebook to illustrate our Private Set Intersection protocol
The Jupyter notebooks server will be kept alive between the periods 08/27 to 10/07 for the reviewers to access and test the notebooks. However, we will reduce the computational capacity for these servers in this period due to their cost. We will improve the computational capacity for the server during the actual demo to accommodate as many participants as possible.