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README.md

Learning private models with multiple teachers

This repository contains code to create a setup for learning privacy-preserving student models by transferring knowledge from an ensemble of teachers trained on disjoint subsets of the data for which privacy guarantees are to be provided.

Knowledge acquired by teachers is transferred to the student in a differentially private manner by noisily aggregating the teacher decisions before feeding them to the student during training.

The paper describing the approach is arXiv:1610.05755

Dependencies

This model uses TensorFlow to perform numerical computations associated with machine learning models, as well as common Python libraries like: numpy, scipy, and six. Instructions to install these can be found in their respective documentations.

How to run

This repository supports the MNIST and SVHN datasets. The following instructions are given for MNIST but can easily be adapted by replacing the flag --dataset=mnist by --dataset=svhn. There are 2 steps: teacher training and student training. Data will be automatically downloaded when you start the teacher training.

The following is a two-step process: first we train an ensemble of teacher models and second we train a student using predictions made by this ensemble.

Training the teachers: first run the train_teachers.py file with at least three flags specifying (1) the number of teachers, (2) the ID of the teacher you are training among these teachers, and (3) the dataset on which to train. For instance, to train teacher number 10 among an ensemble of 100 teachers for MNIST, you use the following command:

python train_teachers.py --nb_teachers=100 --teacher_id=10 --dataset=mnist

Other flags like train_dir and data_dir should optionally be set to respectively point to the directory where model checkpoints and temporary data (like the dataset) should be saved. The flag max_steps (default at 3000) controls the length of training. See train_teachers.py and deep_cnn.py to find available flags and their descriptions.

Training the student: once the teachers are all trained, e.g., teachers with IDs 0 to 99 are trained for nb_teachers=100, we are ready to train the student. The student is trained by labeling some of the test data with predictions from the teachers. The predictions are aggregated by counting the votes assigned to each class among the ensemble of teachers, adding Laplacian noise to these votes, and assigning the label with the maximum noisy vote count to the sample. This is detailed in function noisy_max in the file aggregation.py. To learn the student, use the following command:

python train_student.py --nb_teachers=100 --dataset=mnist --stdnt_share=5000

The flag --stdnt_share=5000 indicates that the student should be able to use the first 5000 samples of the dataset's test subset as unlabeled training points (they will be labeled using the teacher predictions). The remaining samples are used for evaluation of the student's accuracy, which is displayed upon completion of training.

Using semi-supervised GANs to train the student

In the paper, we describe how to train the student in a semi-supervised fashion using Generative Adversarial Networks. This can be reproduced for MNIST by cloning the improved-gan repository and adding to your PATH variable before running the shell script train_student_mnist_250_lap_20_count_50_epochs_600.sh.

export PATH="/path/to/improved-gan/mnist_svhn_cifar10":$PATH
sh train_student_mnist_250_lap_20_count_50_epochs_600.sh

Alternative deeper convolutional architecture

Note that a deeper convolutional model is available. Both the default and deeper models graphs are defined in deep_cnn.py, respectively by functions inference and inference_deeper. Use the flag --deeper=true to switch to that model when launching train_teachers.py and train_student.py.

Privacy analysis

In the paper, we detail how data-dependent differential privacy bounds can be computed to estimate the cost of training the student. In order to reproduce the bounds given in the paper, we include the label predicted by our two teacher ensembles: MNIST and SVHN. You can run the privacy analysis for each dataset with the following commands:

python analysis.py --counts_file=mnist_250_teachers_labels.npy --indices_file=mnist_250_teachers_100_indices_used_by_student.npy

python analysis.py --counts_file=svhn_250_teachers_labels.npy --max_examples=1000 --delta=1e-6

To expedite experimentation with the privacy analysis of student training, the analysis.py file is configured to download the labels produced by 250 teacher models, for MNIST and SVHN when running the two commands included above. These 250 teacher models were trained using the following command lines, where XXX takes values between 0 and 249:

python train_teachers.py --nb_teachers=250 --teacher_id=XXX --dataset=mnist
python train_teachers.py --nb_teachers=250 --teacher_id=XXX --dataset=svhn

Note that these labels may also be used in lieu of function ensemble_preds in train_student.py, to compare the performance of alternative student model architectures and learning techniques. This facilitates future work, by removing the need for training the MNIST and SVHN teacher ensembles when proposing new student training approaches.

Contact

To ask questions, please email nicolas@papernot.fr or open an issue on the tensorflow/models issues tracker. Please assign issues to @npapernot.