# Machine Learning with Differential Privacy in TensorFlow

*Cross-posted from [cleverhans.io](http://www.cleverhans.io/privacy/2019/03/26/machine-learning-with-differential-privacy-in-tensorflow.html)*

Differential privacy is a framework for measuring the privacy guarantees
provided by an algorithm. Through the lens of differential privacy, we can
design machine learning algorithms that responsibly train models on private
data. Learning with differential privacy provides provable guarantees of
privacy, mitigating the risk of exposing sensitive training data in machine
learning. Intuitively, a model trained with differential privacy should not be
affected by any single training example, or small set of training examples, in
its data set.

You may recall our
[previous blog post on PATE](http://www.cleverhans.io/privacy/2018/04/29/privacy-and-machine-learning.html),
an approach that achieves private learning by carefully coordinating the
activity of several different ML models
[[Papernot et al.]](https://arxiv.org/abs/1610.05755). In this post, you will
learn how to train a differentially private model with another approach that
relies on Differentially Private Stochastic Gradient Descent (DP-SGD)
[[Abadi et al.]](https://arxiv.org/abs/1607.00133). DP-SGD and PATE are two
different ways to achieve the same goal of privacy-preserving machine learning.
DP-SGD makes less assumptions about the ML task than PATE, but this comes at the
expense of making modifications to the training algorithm.

Indeed, DP-SGD is a modification of the stochastic gradient descent algorithm,
which is the basis for many optimizers that are popular in machine learning.
Models trained with DP-SGD have provable privacy guarantees expressed in terms
of differential privacy (we will explain what this means at the end of this
post). We will be using the
[TensorFlow Privacy](https://github.com/tensorflow/privacy) library, which
provides an implementation of DP-SGD, to illustrate our presentation of DP-SGD
and provide a hands-on tutorial.

The only prerequisite for following this tutorial is to be able to train a
simple neural network with TensorFlow. If you are not familiar with
convolutional neural networks or how to train them, we recommend reading
[this tutorial first](https://www.tensorflow.org/tutorials/keras/basic_classification)
to get started with TensorFlow and machine learning.

Upon completing the tutorial presented in this post, you will be able to wrap
existing optimizers (e.g., SGD, Adam, ...) into their differentially private
counterparts using TensorFlow (TF) Privacy. You will also learn how to tune the
parameters introduced by differentially private optimization. Finally, we will
learn how to measure the privacy guarantees provided using analysis tools
included in TF Privacy.

## Getting started

Before we get started with DP-SGD and TF Privacy, we need to put together a
script that trains a simple neural network with TensorFlow.

In the interest of keeping this tutorial focused on the privacy aspects of
training, we've included such a script as companion code for this blog post in
the `walkthrough`
[subdirectory](https://github.com/tensorflow/privacy/tree/master/tutorials/walkthrough)
of the `tutorials` found in the
[TensorFlow Privacy](https://github.com/tensorflow/privacy) repository. The code
found in the file `mnist_scratch.py` trains a small convolutional neural network
on the MNIST dataset for handwriting recognition. This script will be used as
the basis for our exercise below.

Next, we highlight some important code snippets from the `mnist_scratch.py`
script.

The first snippet includes the definition of a convolutional neural network
using `tf.keras.layers`. The model contains two convolutional layers coupled
with max pooling layers, a fully-connected layer, and a softmax. The model's
output is a vector where each component indicates how likely the input is to be
in one of the 10 classes of the handwriting recognition problem we considered.
If any of this sounds unfamiliar, we recommend reading
[this tutorial first](https://www.tensorflow.org/tutorials/keras/basic_classification)
to get started with TensorFlow and machine learning.

```python
input_layer = tf.reshape(features['x'], [-1, 28, 28, 1])
y = tf.keras.layers.Conv2D(16, 8,
                           strides=2,
                           padding='same',
                           activation='relu').apply(input_layer)
y = tf.keras.layers.MaxPool2D(2, 1).apply(y)
y = tf.keras.layers.Conv2D(32, 4,
                           strides=2,
                           padding='valid',
                           activation='relu').apply(y)
y = tf.keras.layers.MaxPool2D(2, 1).apply(y)
y = tf.keras.layers.Flatten().apply(y)
y = tf.keras.layers.Dense(32, activation='relu').apply(y)
logits = tf.keras.layers.Dense(10).apply(y)
predicted_labels = tf.argmax(input=logits, axis=1)
```

The second snippet shows how the model is trained using the `tf.Estimator` API,
which takes care of all the boilerplate code required to form minibatches used
to train and evaluate the model. To prepare ourselves for the modifications we
will be making to provide differential privacy, we still expose the loop over
different epochs of learning: an epoch is defined as one pass over all of the
training points included in the training set.

```python
steps_per_epoch = 60000 // FLAGS.batch_size
for epoch in range(1, FLAGS.epochs + 1):
  # Train the model for one epoch.
  mnist_classifier.train(input_fn=train_input_fn, steps=steps_per_epoch)

  # Evaluate the model and print results
  eval_results = mnist_classifier.evaluate(input_fn=eval_input_fn)
  test_accuracy = eval_results['accuracy']
  print('Test accuracy after %d epochs is: %.3f' % (epoch, test_accuracy))
```

We are now ready to train our MNIST model without privacy. The model should
achieve above 99% test accuracy after 15 epochs at a learning rate of 0.15 on
minibatches of 256 training points.

```shell
python mnist_scratch.py
```

### Stochastic Gradient Descent

Before we dive into how DP-SGD and TF Privacy can be used to provide
differential privacy during machine learning, we first provide a brief overview
of the stochastic gradient descent algorithm, which is one of the most popular
optimizers for neural networks.

Stochastic gradient descent is an iterative procedure. At each iteration, a
batch of data is randomly sampled from the training set (this is where the
*stochasticity* comes from). The error between the model's prediction and the
training labels is then computed. This error, also called the loss, is then
differentiated with respect to the model's parameters. These derivatives (or
*gradients*) tell us how we should update each parameter to bring the model
closer to predicting the correct label. Iteratively recomputing gradients and
applying them to update the model's parameters is what is referred to as the
*descent*. To summarize, the following steps are repeated until the model's
performance is satisfactory:

1.  Sample a minibatch of training points `(x, y)` where `x` is an input and `y`
    a label.

2.  Compute loss (i.e., error) `L(theta, x, y)` between the model's prediction
    `f_theta(x)` and label `y` where `theta` represents the model parameters.

3.  Compute gradient of the loss `L(theta, x, y)` with respect to the model
    parameters `theta`.

4.  Multiply these gradients by the learning rate and apply the product to
    update model parameters `theta`.

### Modifications needed to make stochastic gradient descent a differentially private algorithm

Two modifications are needed to ensure that stochastic gradient descent is a
differentially private algorithm.

First, the sensitivity of each gradient needs to be bounded. In other words, we
need to limit how much each individual training point sampled in a minibatch can
influence the resulting gradient computation. This can be done by clipping each
gradient computed on each training point between steps 3 and 4 above.
Intuitively, this allows us to bound how much each training point can possibly
impact model parameters.

Second, we need to randomize the algorithm's behavior to make it statistically
impossible to know whether or not a particular point was included in the
training set by comparing the updates stochastic gradient descent applies when
it operates with or without this particular point in the training set. This is
achieved by sampling random noise and adding it to the clipped gradients.

Thus, here is the stochastic gradient descent algorithm adapted from above to be
differentially private:

1.  Sample a minibatch of training points `(x, y)` where `x` is an input and `y`
    a label.

2.  Compute loss (i.e., error) `L(theta, x, y)` between the model's prediction
    `f_theta(x)` and label `y` where `theta` represents the model parameters.

3.  Compute gradient of the loss `L(theta, x, y)` with respect to the model
    parameters `theta`.

4.  Clip gradients, per training example included in the minibatch, to ensure
    each gradient has a known maximum Euclidean norm.

5.  Add random noise to the clipped gradients.

6.  Multiply these clipped and noised gradients by the learning rate and apply
    the product to update model parameters `theta`.

### Implementing DP-SGD with TF Privacy

It's now time to make changes to the code we started with to take into account
the two modifications outlined in the previous paragraph: gradient clipping and
noising. This is where TF Privacy kicks in: it provides code that wraps an
existing TF optimizer to create a variant that performs both of these steps
needed to obtain differential privacy.

As mentioned above, step 1 of the algorithm, that is forming minibatches of
training data and labels, is implemented by the `tf.Estimator` API in our
tutorial. We can thus go straight to step 2 of the algorithm outlined above and
compute the loss (i.e., model error) between the model's predictions and labels.

```python
vector_loss = tf.nn.sparse_softmax_cross_entropy_with_logits(
    labels=labels, logits=logits)
```

TensorFlow provides implementations of common losses, here we use the
cross-entropy, which is well-suited for our classification problem. Note how we
computed the loss as a vector, where each component of the vector corresponds to
an individual training point and label. This is required to support per example
gradient manipulation later at step 4.

We are now ready to create an optimizer. In TensorFlow, an optimizer object can
be instantiated by passing it a learning rate value, which is used in step 6
outlined above. This is what the code would look like *without* differential
privacy:

```python
optimizer = tf.train.GradientDescentOptimizer(FLAGS.learning_rate)
train_op = optimizer.minimize(loss=scalar_loss)
```

Note that our code snippet assumes that a TensorFlow flag was defined for the
learning rate value.

Now, we use the `optimizers.dp_optimizer` module of TF Privacy to implement the
optimizer with differential privacy. Under the hood, this code implements steps
3-6 of the algorithm above:

```python
optimizer = optimizers.dp_optimizer.DPGradientDescentGaussianOptimizer(
    l2_norm_clip=FLAGS.l2_norm_clip,
    noise_multiplier=FLAGS.noise_multiplier,
    num_microbatches=FLAGS.microbatches,
    learning_rate=FLAGS.learning_rate,
    population_size=60000)
train_op = optimizer.minimize(loss=vector_loss)
```

In these two code snippets, we used the stochastic gradient descent optimizer
but it could be replaced by another optimizer implemented in TensorFlow. For
instance, the `AdamOptimizer` can be replaced by `DPAdamGaussianOptimizer`. In
addition to the standard optimizers already included in TF Privacy, most
optimizers which are objects from a child class of `tf.train.Optimizer` can be
made differentially private by calling
`optimizers.dp_optimizer.make_gaussian_optimizer_class()`.

As you can see, only one line needs to change but there are a few things going
on that are best to unwrap before we continue. In addition to the learning rate,
we passed the size of the training set as the `population_size` parameter. This
is used to measure the strength of privacy achieved; we will come back to this
accounting aspect later.

More importantly, TF Privacy introduces three new hyperparameters to the
optimizer object: `l2_norm_clip`, `noise_multiplier`, and `num_microbatches`.
You may have deduced what `l2_norm_clip` and `noise_multiplier` are from the two
changes outlined above.

Parameter `l2_norm_clip` is the maximum Euclidean norm of each individual
gradient that is computed on an individual training example from a minibatch.
This parameter is used to bound the optimizer's sensitivity to individual
training points. Note how in order for the optimizer to be able to compute these
per example gradients, we must pass it a *vector* loss as defined previously,
rather than the loss averaged over the entire minibatch.

Next, the `noise_multiplier` parameter is used to control how much noise is
sampled and added to gradients before they are applied by the optimizer.
Generally, more noise results in better privacy (often, but not necessarily, at
the expense of lower utility).

The third parameter relates to an aspect of DP-SGD that was not discussed
previously. In practice, clipping gradients on a per example basis can be
detrimental to the performance of our approach because computations can no
longer be batched and parallelized at the granularity of minibatches. Hence, we
introduce a new granularity by splitting each minibatch into multiple
microbatches [[McMahan et al.]](https://arxiv.org/abs/1812.06210). Rather than
clipping gradients on a per example basis, we clip them on a microbatch basis.
For instance, if we have a minibatch of 256 training examples, rather than
clipping each of the 256 gradients individually, we would clip 32 gradients
averaged over microbatches of 8 training examples when `num_microbatches=32`.
This allows for some degree of parallelism. Hence, one can think of
`num_microbatches` as a parameter that allows us to trade off performance (when
the parameter is set to a small value) with utility (when the parameter is set
to a value close to the minibatch size).

Once you've implemented all these changes, try training your model again with
the differentially private stochastic gradient optimizer. You can use the
following hyperparameter values to obtain a reasonable model (95% test
accuracy):

```python
learning_rate=0.25
noise_multiplier=1.3
l2_norm_clip=1.5
batch_size=256
epochs=15
num_microbatches=256
```

### Measuring the privacy guarantee achieved

At this point, we made all the changes needed to train our model with
differential privacy. Congratulations! Yet, we are still missing one crucial
piece of the puzzle: we have not computed the privacy guarantee achieved. Recall
the two modifications we made to the original stochastic gradient descent
algorithm: clip and randomize gradients.

It is intuitive to machine learning practitioners how clipping gradients limits
the ability of the model to overfit to any of its training points. In fact,
gradient clipping is commonly employed in machine learning even when privacy is
not a concern. The intuition for introducing randomness to a learning algorithm
that is already randomized is a little more subtle but this additional
randomization is required to make it hard to tell which behavioral aspects of
the model defined by the learned parameters came from randomness and which came
from the training data. Without randomness, we would be able to ask questions
like: “What parameters does the learning algorithm choose when we train it on
this specific dataset?” With randomness in the learning algorithm, we instead
ask questions like: “What is the probability that the learning algorithm will
choose parameters in this set of possible parameters, when we train it on this
specific dataset?”

We use a version of differential privacy which requires that the probability of
learning any particular set of parameters stays roughly the same if we change a
single training example in the training set. This could mean to add a training
example, remove a training example, or change the values within one training
example. The intuition is that if a single training point does not affect the
outcome of learning, the information contained in that training point cannot be
memorized and the privacy of the individual who contributed this data point to
our dataset is respected. We often refer to this probability as the privacy
budget: smaller privacy budgets correspond to stronger privacy guarantees.

Accounting required to compute the privacy budget spent to train our machine
learning model is another feature provided by TF Privacy. Knowing what level of
differential privacy was achieved allows us to put into perspective the drop in
utility that is often observed when switching to differentially private
optimization. It also allows us to compare two models objectively to determine
which of the two is more privacy-preserving than the other.

Before we derive a bound on the privacy guarantee achieved by our optimizer, we
first need to identify all the parameters that are relevant to measuring the
potential privacy loss induced by training. These are the `noise_multiplier`,
the sampling ratio `q` (the probability of an individual training point being
included in a minibatch), and the number of `steps` the optimizer takes over the
training data. We simply report the `noise_multiplier` value provided to the
optimizer and compute the sampling ratio and number of steps as follows:

```python
noise_multiplier = FLAGS.noise_multiplier
sampling_probability = FLAGS.batch_size / 60000
steps = FLAGS.epochs * 60000 // FLAGS.batch_size
```

At a high level, the privacy analysis measures how including or excluding any
particular point in the training data is likely to change the probability that
we learn any particular set of parameters. In other words, the analysis measures
the difference between the distributions of model parameters on neighboring
training sets (pairs of any training sets with a Hamming distance of 1). In TF
Privacy, we use the Rényi divergence to measure this distance between
distributions. Indeed, our analysis is performed in the framework of Rényi
Differential Privacy (RDP), which is a generalization of pure differential
privacy [[Mironov]](https://arxiv.org/abs/1702.07476). RDP is a useful tool here
because it is particularly well suited to analyze the differential privacy
guarantees provided by sampling followed by Gaussian noise addition, which is
how gradients are randomized in the TF Privacy implementation of the DP-SGD
optimizer.

We will express our differential privacy guarantee using two parameters:
`epsilon` and `delta`.

*   Delta bounds the probability of our privacy guarantee not holding. A rule of
    thumb is to set it to be less than the inverse of the training data size
    (i.e., the population size). Here, we set it to `10^-5` because MNIST has
    60000 training points.

*   Epsilon measures the strength of our privacy guarantee. In the case of
    differentially private machine learning, it gives a bound on how much the
    probability of a particular model output can vary by including (or removing)
    a single training example. We usually want it to be a small constant.
    However, this is only an upper bound, and a large value of epsilon could
    still mean good practical privacy.

The TF Privacy library provides two methods relevant to derive privacy
guarantees achieved from the three parameters outlined in the last code snippet:
`compute_rdp` and `get_privacy_spent`. These methods are found in its
`analysis.rdp_accountant` module. Here is how to use them.

First, we need to define a list of orders, at which the Rényi divergence will be
computed. While some finer points of how to use the RDP accountant are outside
the scope of this document, it is useful to keep in mind the following. First,
there is very little downside in expanding the list of orders for which RDP is
computed. Second, the computed privacy budget is typically not very sensitive to
the exact value of the order (being close enough will land you in the right
neighborhood). Finally, if you are targeting a particular range of epsilons
(say, 1—10) and your delta is fixed (say, `10^-5`), then your orders must cover
the range between `1+ln(1/delta)/10≈2.15` and `1+ln(1/delta)/1≈12.5`. This last
rule may appear circular (how do you know what privacy parameters you get
without running the privacy accountant?!), one or two adjustments of the range
of the orders would usually suffice.

```python
orders = [1 + x / 10. for x in range(1, 100)] + list(range(12, 64))
rdp = compute_rdp(q=sampling_probability,
                  noise_multiplier=FLAGS.noise_multiplier,
                  steps=steps,
                  orders=orders)
```

Then, the method `get_privacy_spent` computes the best `epsilon` for a given
`target_delta` value of delta by taking the minimum over all orders.

```python
epsilon = get_privacy_spent(orders, rdp, target_delta=1e-5)[0]
```

Running the code snippets above with the hyperparameter values used during
training will estimate the `epsilon` value that was achieved by the
differentially private optimizer, and thus the strength of the privacy guarantee
which comes with the model we trained. Once we computed the value of `epsilon`,
interpreting this value is at times difficult. One possibility is to purposely
insert secrets in the model's training set and measure how likely they are to be
leaked by a differentially private model (compared to a non-private model) at
inference time [[Carlini et al.]](https://arxiv.org/abs/1802.08232).

### Putting all the pieces together

We covered a lot in this blog post! If you made all the changes discussed
directly into the `mnist_scratch.py` file, you should have been able to train a
differentially private neural network on MNIST and measure the privacy guarantee
achieved.

However, in case you ran into an issue or you'd like to see what a complete
implementation looks like, the "solution" to the tutorial presented in this blog
post can be
[found](https://github.com/tensorflow/privacy/blob/master/tutorials/mnist_dpsgd_tutorial.py)
in the tutorials directory of TF Privacy. It is the script called
`mnist_dpsgd_tutorial.py`.
