How non-public are particular person knowledge within the context of machine studying fashions? The info used to coach the mannequin, say. There are
sorts of fashions the place the reply is easy. Take k-nearest-neighbors, for instance. There just isn’t even a mannequin with out the
full dataset. Or help vector machines. There is no such thing as a mannequin with out the help vectors. However neural networks? They’re simply
some composition of features, – no knowledge included.
The identical is true for knowledge fed to a deployed deep-learning mannequin. It’s fairly unlikely one may invert the ultimate softmax
output from a giant ResNet and get again the uncooked enter knowledge.
In idea, then, “hacking” an ordinary neural internet to spy on enter knowledge sounds illusory. In observe, nevertheless, there may be at all times
some real-world context. The context could also be different datasets, publicly accessible, that may be linked to the “non-public” knowledge in
query. It is a fashionable showcase utilized in advocating for differential privateness(Dwork et al. 2006): Take an “anonymized” dataset,
dig up complementary info from public sources, and de-anonymize data advert libitum. Some context in that sense will
usually be utilized in “black-box” assaults, ones that presuppose no insider details about the mannequin to be hacked.
However context will also be structural, similar to within the situation demonstrated on this publish. For instance, assume a distributed
mannequin, the place units of layers run on completely different gadgets – embedded gadgets or cell phones, for instance. (A situation like that
is typically seen as “white-box”(Wu et al. 2016), however in widespread understanding, white-box assaults most likely presuppose some extra
insider data, similar to entry to mannequin structure and even, weights. I’d due to this fact choose calling this white-ish at
most.) — Now assume that on this context, it’s doable to intercept, and work together with, a system that executes the deeper
layers of the mannequin. Primarily based on that system’s intermediate-level output, it’s doable to carry out mannequin inversion(Fredrikson et al. 2014),
that’s, to reconstruct the enter knowledge fed into the system.
On this publish, we’ll display such a mannequin inversion assault, principally porting the method given in a
pocket book
discovered within the PySyft repository. We then experiment with completely different ranges of
(epsilon)-privacy, exploring affect on reconstruction success. This second half will make use of TensorFlow Privateness,
launched in a earlier weblog publish.
Half 1: Mannequin inversion in motion
Instance dataset: All of the world’s letters
The general technique of mannequin inversion used right here is the next. With no, or scarcely any, insider data a couple of mannequin,
– however given alternatives to repeatedly question it –, I wish to discover ways to reconstruct unknown inputs primarily based on simply mannequin
outputs . Independently of authentic mannequin coaching, this, too, is a coaching course of; nevertheless, generally it is not going to contain
the unique knowledge, as these received’t be publicly accessible. Nonetheless, for finest success, the attacker mannequin is educated with knowledge as
comparable as doable to the unique coaching knowledge assumed. Considering of photos, for instance, and presupposing the favored view
of successive layers representing successively coarse-grained options, we wish that the surrogate knowledge to share as many
illustration areas with the actual knowledge as doable – as much as the very highest layers earlier than last classification, ideally.
If we needed to make use of classical MNIST for example, one factor we may do is to solely use among the digits for coaching the
“actual” mannequin; and the remaining, for coaching the adversary. Let’s attempt one thing completely different although, one thing that may make the
endeavor tougher in addition to simpler on the identical time. More durable, as a result of the dataset options exemplars extra complicated than MNIST
digits; simpler due to the identical cause: Extra may presumably be realized, by the adversary, from a posh activity.
Initially designed to develop a machine mannequin of idea studying and generalization (Lake, Salakhutdinov, and Tenenbaum 2015), the
OmniGlot dataset incorporates characters from fifty alphabets, cut up into two
disjoint teams of thirty and twenty alphabets every. We’ll use the group of twenty to coach our goal mannequin. Here’s a
pattern:
Determine 1: Pattern from the twenty-alphabet set used to coach the goal mannequin (initially: ‘analysis set’)
The group of thirty we don’t use; as a substitute, we’ll make use of two small five-alphabet collections to coach the adversary and to check
reconstruction, respectively. (These small subsets of the unique “massive” thirty-alphabet set are once more disjoint.)
Right here first is a pattern from the set used to coach the adversary.
Determine 2: Pattern from the five-alphabet set used to coach the adversary (initially: ‘background small 1’)
The opposite small subset might be used to check the adversary’s spying capabilities after coaching. Let’s peek at this one, too:
Determine 3: Pattern from the five-alphabet set used to check the adversary after coaching(initially: ‘background small 2’)
Conveniently, we are able to use tfds, the R wrapper to TensorFlow Datasets, to load these subsets:
Now first, we practice the goal mannequin.
Prepare goal mannequin
The dataset initially has 4 columns: the picture, of measurement 105 x 105; an alphabet id and a within-dataset character id; and a
label. For our use case, we’re not likely within the activity the goal mannequin was/is used for; we simply wish to get on the
knowledge. Principally, no matter activity we select, it isn’t rather more than a dummy activity. So, let’s simply say we practice the goal to
classify characters by alphabet.
We thus throw out all unneeded options, retaining simply the alphabet id and the picture itself:
# normalize and work with a single channel (photos are black-and-white anyway)
preprocess_image <- perform(picture) {
picture %>%
tf$forged(dtype = tf$float32) %>%
tf$truediv(y = 255) %>%
tf$picture$rgb_to_grayscale()
}
# use the primary 11000 photos for coaching
train_ds <- omni_train %>%
dataset_take(11000) %>%
dataset_map(perform(report) {
report$picture <- preprocess_image(report$picture)
checklist(report$picture, report$alphabet)}) %>%
dataset_shuffle(1000) %>%
dataset_batch(32)
# use the remaining 2180 data for validation
val_ds <- omni_train %>%
dataset_skip(11000) %>%
dataset_map(perform(report) {
report$picture <- preprocess_image(report$picture)
checklist(report$picture, report$alphabet)}) %>%
dataset_batch(32)The mannequin consists of two elements. The primary is imagined to run in a distributed style; for instance, on cellular gadgets (stage
one). These gadgets then ship mannequin outputs to a central server, the place last outcomes are computed (stage two). Certain, you’ll
be considering, this can be a handy setup for our situation: If we intercept stage one outcomes, we – likely – acquire
entry to richer info than what’s contained in a mannequin’s last output layer. — That’s appropriate, however the situation is
much less contrived than one may assume. Identical to federated studying (McMahan et al. 2016), it fulfills necessary desiderata: Precise
coaching knowledge by no means leaves the gadgets, thus staying (in idea!) non-public; on the identical time, ingoing visitors to the server is
considerably decreased.
In our instance setup, the on-device mannequin is a convnet, whereas the server mannequin is an easy feedforward community.
We hyperlink each collectively as a TargetModel that when referred to as usually, will run each steps in succession. Nevertheless, we’ll give you the chance
to name target_model$mobile_step() individually, thereby intercepting intermediate outcomes.
on_device_model <- keras_model_sequential() %>%
layer_conv_2d(filters = 32, kernel_size = c(7, 7),
input_shape = c(105, 105, 1), activation = "relu") %>%
layer_batch_normalization() %>%
layer_max_pooling_2d(pool_size = c(3, 3), strides = 3) %>%
layer_dropout(0.2) %>%
layer_conv_2d(filters = 32, kernel_size = c(7, 7), activation = "relu") %>%
layer_batch_normalization() %>%
layer_max_pooling_2d(pool_size = c(3, 3), strides = 2) %>%
layer_dropout(0.2) %>%
layer_conv_2d(filters = 32, kernel_size = c(5, 5), activation = "relu") %>%
layer_batch_normalization() %>%
layer_max_pooling_2d(pool_size = c(2, 2), strides = 2) %>%
layer_dropout(0.2) %>%
layer_conv_2d(filters = 32, kernel_size = c(3, 3), activation = "relu") %>%
layer_batch_normalization() %>%
layer_max_pooling_2d(pool_size = c(2, 2), strides = 2) %>%
layer_dropout(0.2)
server_model <- keras_model_sequential() %>%
layer_dense(items = 256, activation = "relu") %>%
layer_flatten() %>%
layer_dropout(0.2) %>%
# we've simply 20 completely different ids, however they don't seem to be in lexicographic order
layer_dense(items = 50, activation = "softmax")
target_model <- perform() {
keras_model_custom(title = "TargetModel", perform(self) {
self$on_device_model <-on_device_model
self$server_model <- server_model
self$mobile_step <- perform(inputs)
self$on_device_model(inputs)
self$server_step <- perform(inputs)
self$server_model(inputs)
perform(inputs, masks = NULL) {
inputs %>%
self$mobile_step() %>%
self$server_step()
}
})
}
mannequin <- target_model()The general mannequin is a Keras customized mannequin, so we practice it TensorFlow 2.x –
type. After ten epochs, coaching and validation accuracy are at ~0.84
and ~0.73, respectively – not dangerous in any respect for a 20-class discrimination activity.
loss <- loss_sparse_categorical_crossentropy
optimizer <- optimizer_adam()
train_loss <- tf$keras$metrics$Imply(title='train_loss')
train_accuracy <- tf$keras$metrics$SparseCategoricalAccuracy(title='train_accuracy')
val_loss <- tf$keras$metrics$Imply(title='val_loss')
val_accuracy <- tf$keras$metrics$SparseCategoricalAccuracy(title='val_accuracy')
train_step <- perform(photos, labels) {
with (tf$GradientTape() %as% tape, {
predictions <- mannequin(photos)
l <- loss(labels, predictions)
})
gradients <- tape$gradient(l, mannequin$trainable_variables)
optimizer$apply_gradients(purrr::transpose(checklist(
gradients, mannequin$trainable_variables
)))
train_loss(l)
train_accuracy(labels, predictions)
}
val_step <- perform(photos, labels) {
predictions <- mannequin(photos)
l <- loss(labels, predictions)
val_loss(l)
val_accuracy(labels, predictions)
}
training_loop <- tf_function(autograph(perform(train_ds, val_ds) {
for (b1 in train_ds) {
train_step(b1[[1]], b1[[2]])
}
for (b2 in val_ds) {
val_step(b2[[1]], b2[[2]])
}
tf$print("Prepare accuracy", train_accuracy$end result(),
" Validation Accuracy", val_accuracy$end result())
train_loss$reset_states()
train_accuracy$reset_states()
val_loss$reset_states()
val_accuracy$reset_states()
}))
for (epoch in 1:10) {
cat("Epoch: ", epoch, " -----------n")
training_loop(train_ds, val_ds)
}Epoch: 1 -----------
Prepare accuracy 0.195090905 Validation Accuracy 0.376605511
Epoch: 2 -----------
Prepare accuracy 0.472272724 Validation Accuracy 0.5243119
...
...
Epoch: 9 -----------
Prepare accuracy 0.821454525 Validation Accuracy 0.720183492
Epoch: 10 -----------
Prepare accuracy 0.840454519 Validation Accuracy 0.726605475Now, we practice the adversary.
Prepare adversary
The adversary’s basic technique might be:
- Feed its small, surrogate dataset to the on-device mannequin. The output acquired will be considered a (extremely)
compressed model of the unique photos. - Pass that “compressed” model as enter to its personal mannequin, which tries to reconstruct the unique photos from the
sparse code. - Evaluate authentic photos (these from the surrogate dataset) to the reconstruction pixel-wise. The objective is to reduce
the imply (squared, say) error.
Doesn’t this sound lots just like the decoding aspect of an autoencoder? No surprise the attacker mannequin is a deconvolutional community.
Its enter – equivalently, the on-device mannequin’s output – is of measurement batch_size x 1 x 1 x 32. That’s, the data is
encoded in 32 channels, however the spatial decision is 1. Identical to in an autoencoder working on photos, we have to
upsample till we arrive on the authentic decision of 105 x 105.
That is precisely what’s taking place within the attacker mannequin:
attack_model <- perform() {
keras_model_custom(title = "AttackModel", perform(self) {
self$conv1 <-layer_conv_2d_transpose(filters = 32, kernel_size = 9,
padding = "legitimate",
strides = 1, activation = "relu")
self$conv2 <- layer_conv_2d_transpose(filters = 32, kernel_size = 7,
padding = "legitimate",
strides = 2, activation = "relu")
self$conv3 <- layer_conv_2d_transpose(filters = 1, kernel_size = 7,
padding = "legitimate",
strides = 2, activation = "relu")
self$conv4 <- layer_conv_2d_transpose(filters = 1, kernel_size = 5,
padding = "legitimate",
strides = 2, activation = "relu")
perform(inputs, masks = NULL) {
inputs %>%
# bs * 9 * 9 * 32
# output = strides * (enter - 1) + kernel_size - 2 * padding
self$conv1() %>%
# bs * 23 * 23 * 32
self$conv2() %>%
# bs * 51 * 51 * 1
self$conv3() %>%
# bs * 105 * 105 * 1
self$conv4()
}
})
}
attacker = attack_model()To coach the adversary, we use one of many small (five-alphabet) subsets. To reiterate what was mentioned above, there isn’t a overlap
with the information used to coach the goal mannequin.
Right here, then, is the attacker coaching loop, striving to refine the decoding course of over 100 – brief – epochs:
attacker_criterion <- loss_mean_squared_error
attacker_optimizer <- optimizer_adam()
attacker_loss <- tf$keras$metrics$Imply(title='attacker_loss')
attacker_mse <- tf$keras$metrics$MeanSquaredError(title='attacker_mse')
attacker_step <- perform(photos) {
attack_input <- mannequin$mobile_step(photos)
with (tf$GradientTape() %as% tape, {
generated <- attacker(attack_input)
l <- attacker_criterion(photos, generated)
})
gradients <- tape$gradient(l, attacker$trainable_variables)
attacker_optimizer$apply_gradients(purrr::transpose(checklist(
gradients, attacker$trainable_variables
)))
attacker_loss(l)
attacker_mse(photos, generated)
}
attacker_training_loop <- tf_function(autograph(perform(attacker_ds) {
for (b in attacker_ds) {
attacker_step(b[[1]])
}
tf$print("mse: ", attacker_mse$end result())
attacker_loss$reset_states()
attacker_mse$reset_states()
}))
for (epoch in 1:100) {
cat("Epoch: ", epoch, " -----------n")
attacker_training_loop(attacker_ds)
}Epoch: 1 -----------
mse: 0.530902684
Epoch: 2 -----------
mse: 0.201351956
...
...
Epoch: 99 -----------
mse: 0.0413453057
Epoch: 100 -----------
mse: 0.0413028933The query now’s, – does it work? Has the attacker actually realized to deduce precise knowledge from (stage one) mannequin output?
Check adversary
To check the adversary, we use the third dataset we downloaded, containing photos from 5 yet-unseen alphabets. For show,
we choose simply the primary sixteen data – a totally arbitrary resolution, in fact.
test_ds <- omni_test %>%
dataset_map(perform(report) {
report$picture <- preprocess_image(report$picture)
checklist(report$picture, report$alphabet)}) %>%
dataset_take(16) %>%
dataset_batch(16)
batch <- as_iterator(test_ds) %>% iterator_get_next()
photos <- batch[[1]]
attack_input <- mannequin$mobile_step(photos)
generated <- attacker(attack_input) %>% as.array()
generated[generated > 1] <- 1
generated <- generated[ , , , 1]
generated %>%
purrr::array_tree(1) %>%
purrr::map(as.raster) %>%
purrr::iwalk(~{plot(.x)})Identical to through the coaching course of, the adversary queries the goal mannequin (stage one), obtains the compressed
illustration, and makes an attempt to reconstruct the unique picture. (In fact, in the actual world, the setup could be completely different in
that the attacker would not have the ability to merely examine the pictures, as is the case right here. There would thus must be a way
to intercept, and make sense of, community visitors.)
To permit for simpler comparability (and enhance suspense …!), right here once more are the precise photos, which we displayed already when
introducing the dataset:
Determine 4: First photos from the check set, the way in which they actually look.
And right here is the reconstruction:
Determine 5: First photos from the check set, as reconstructed by the adversary.
In fact, it’s exhausting to say how revealing these “guesses” are. There positively appears to be a connection to character
complexity; general, it looks like the Greek and Roman letters, that are the least complicated, are additionally those most simply
reconstructed. Nonetheless, in the long run, how a lot privateness is misplaced will very a lot rely on contextual components.
In the beginning, do the exemplars within the dataset symbolize people or courses of people? If – as in actuality
– the character X represents a category, it won’t be so grave if we have been capable of reconstruct “some X” right here: There are a lot of
Xs within the dataset, all fairly comparable to one another; we’re unlikely to precisely to have reconstructed one particular, particular person
X. If, nevertheless, this was a dataset of particular person individuals, with all Xs being images of Alex, then in reconstructing an
X we’ve successfully reconstructed Alex.
Second, in much less apparent eventualities, evaluating the diploma of privateness breach will seemingly surpass computation of quantitative
metrics, and contain the judgment of area consultants.
Talking of quantitative metrics although – our instance looks like an ideal use case to experiment with differential
privateness. Differential privateness is measured by (epsilon) (decrease is healthier), the principle thought being that solutions to queries to a
system ought to rely as little as doable on the presence or absence of a single (any single) datapoint.
So, we are going to repeat the above experiment, utilizing TensorFlow Privateness (TFP) so as to add noise, in addition to clip gradients, throughout
optimization of the goal mannequin. We’ll attempt three completely different circumstances, leading to three completely different values for (epsilon)s,
and for every situation, examine the pictures reconstructed by the adversary.
Half 2: Differential privateness to the rescue
Sadly, the setup for this a part of the experiment requires somewhat workaround. Making use of the flexibleness afforded
by TensorFlow 2.x, our goal mannequin has been a customized mannequin, becoming a member of two distinct levels (“cellular” and “server”) that may very well be
referred to as independently.
TFP, nevertheless, does nonetheless not work with TensorFlow 2.x, that means we’ve to make use of old-style, non-eager mannequin definitions and
coaching. Fortunately, the workaround might be simple.
First, load (and presumably, set up) libraries, taking care to disable TensorFlow V2 conduct.
The coaching set is loaded, preprocessed and batched (almost) as earlier than.
omni_train <- tfds$load("omniglot", cut up = "check")
batch_size <- 32
train_ds <- omni_train %>%
dataset_take(11000) %>%
dataset_map(perform(report) {
report$picture <- preprocess_image(report$picture)
checklist(report$picture, report$alphabet)}) %>%
dataset_shuffle(1000) %>%
# want dataset_repeat() when not keen
dataset_repeat() %>%
dataset_batch(batch_size)Prepare goal mannequin – with TensorFlow Privateness
To coach the goal, we put the layers from each levels – “cellular” and “server” – into one sequential mannequin. Observe how we
take away the dropout. It’s because noise might be added throughout optimization anyway.
complete_model <- keras_model_sequential() %>%
layer_conv_2d(filters = 32, kernel_size = c(7, 7),
input_shape = c(105, 105, 1),
activation = "relu") %>%
layer_batch_normalization() %>%
layer_max_pooling_2d(pool_size = c(3, 3), strides = 3) %>%
#layer_dropout(0.2) %>%
layer_conv_2d(filters = 32, kernel_size = c(7, 7), activation = "relu") %>%
layer_batch_normalization() %>%
layer_max_pooling_2d(pool_size = c(3, 3), strides = 2) %>%
#layer_dropout(0.2) %>%
layer_conv_2d(filters = 32, kernel_size = c(5, 5), activation = "relu") %>%
layer_batch_normalization() %>%
layer_max_pooling_2d(pool_size = c(2, 2), strides = 2) %>%
#layer_dropout(0.2) %>%
layer_conv_2d(filters = 32, kernel_size = c(3, 3), activation = "relu") %>%
layer_batch_normalization() %>%
layer_max_pooling_2d(pool_size = c(2, 2), strides = 2, title = "mobile_output") %>%
#layer_dropout(0.2) %>%
layer_dense(items = 256, activation = "relu") %>%
layer_flatten() %>%
#layer_dropout(0.2) %>%
layer_dense(items = 50, activation = "softmax")Utilizing TFP primarily means utilizing a TFP optimizer, one which clips gradients in response to some outlined magnitude and provides noise of
outlined measurement. noise_multiplier is the parameter we’re going to fluctuate to reach at completely different (epsilon)s:
l2_norm_clip <- 1
# ratio of the usual deviation to the clipping norm
# we run coaching for every of the three values
noise_multiplier <- 0.7
noise_multiplier <- 0.5
noise_multiplier <- 0.3
# identical as batch measurement
num_microbatches <- k_cast(batch_size, "int32")
learning_rate <- 0.005
optimizer <- tfp$DPAdamGaussianOptimizer(
l2_norm_clip = l2_norm_clip,
noise_multiplier = noise_multiplier,
num_microbatches = num_microbatches,
learning_rate = learning_rate
)In coaching the mannequin, the second necessary change for TFP we have to make is to have loss and gradients computed on the
particular person degree.
# want so as to add noise to each particular person contribution
loss <- tf$keras$losses$SparseCategoricalCrossentropy(discount = tf$keras$losses$Discount$NONE)
complete_model %>% compile(loss = loss, optimizer = optimizer, metrics = "sparse_categorical_accuracy")
num_epochs <- 20
n_train <- 13180
historical past <- complete_model %>% match(
train_ds,
# want steps_per_epoch when not in keen mode
steps_per_epoch = n_train/batch_size,
epochs = num_epochs)To check three completely different (epsilon)s, we run this thrice, every time with a special noise_multiplier. Every time we arrive at
a special last accuracy.
Here’s a synopsis, the place (epsilon) was computed like so:
compute_priv <- tfp$privateness$evaluation$compute_dp_sgd_privacy
compute_priv$compute_dp_sgd_privacy(
# variety of data in coaching set
n_train,
batch_size,
# noise_multiplier
0.7, # or 0.5, or 0.3
# variety of epochs
20,
# delta - mustn't exceed 1/variety of examples in coaching set
1e-5)| 0.7 | 4.0 | 0.37 |
| 0.5 | 12.5 | 0.45 |
| 0.3 | 84.7 | 0.56 |
Now, because the adversary received’t name the entire mannequin, we have to “minimize off” the second-stage layers. This leaves us with a mannequin
that executes stage-one logic solely. We save its weights, so we are able to later name it from the adversary:
intercepted <- keras_model(
complete_model$enter,
complete_model$get_layer("mobile_output")$output
)
intercepted %>% save_model_hdf5("./intercepted.hdf5")Prepare adversary (towards differentially non-public goal)
In coaching the adversary, we are able to preserve many of the authentic code – that means, we’re again to TF-2 type. Even the definition of
the goal mannequin is similar as earlier than:
on_device_model <- keras_model_sequential() %>%
[...]
server_model <- keras_model_sequential() %>%
[...]
target_model <- perform() {
keras_model_custom(title = "TargetModel", perform(self) {
self$on_device_model <-on_device_model
self$server_model <- server_model
self$mobile_step <- perform(inputs)
self$on_device_model(inputs)
self$server_step <- perform(inputs)
self$server_model(inputs)
perform(inputs, masks = NULL) {
inputs %>%
self$mobile_step() %>%
self$server_step()
}
})
}
intercepted <- target_model()However now, we load the educated goal’s weights into the freshly outlined mannequin’s “cellular stage”:
intercepted$on_device_model$load_weights("intercepted.hdf5")And now, we’re again to the outdated coaching routine. Testing setup is similar as earlier than, as nicely.
So how nicely does the adversary carry out with differential privateness added to the image?
Check adversary (towards differentially non-public goal)
Right here, ordered by lowering (epsilon), are the reconstructions. Once more, we chorus from judging the outcomes, for a similar
causes as earlier than: In real-world functions, whether or not privateness is preserved “nicely sufficient” will rely on the context.
Right here, first, are reconstructions from the run the place the least noise was added.
Determine 6: Reconstruction makes an attempt from a setup the place the goal mannequin was educated with an epsilon of 84.7.
On to the following degree of privateness safety:
Determine 7: Reconstruction makes an attempt from a setup the place the goal mannequin was educated with an epsilon of 12.5.
And the highest-(epsilon) one:
Determine 8: Reconstruction makes an attempt from a setup the place the goal mannequin was educated with an epsilon of 4.0.
Conclusion
All through this publish, we’ve shunned “over-commenting” on outcomes, and targeted on the why-and-how as a substitute. That is
as a result of in a man-made setup, chosen to facilitate exposition of ideas and strategies, there actually isn’t any goal body of
reference. What is an efficient reconstruction? What is an efficient (epsilon)? What constitutes a knowledge breach? No-one is aware of.
In the actual world, there’s a context to all the things – there are individuals concerned, the individuals whose knowledge we’re speaking about.
There are organizations, rules, legal guidelines. There are summary rules, and there are implementations; completely different
implementations of the identical “thought” can differ.
As in machine studying general, analysis papers on privacy-, ethics- or in any other case society-related subjects are stuffed with LaTeX
formulae. Amid the maths, let’s not overlook the individuals.
Thanks for studying!
Fredrikson, Matthew, Eric Lantz, Somesh Jha, Simon Lin, David Web page, and Thomas Ristenpart. 2014. “Privateness in Pharmacogenetics: An Finish-to-Finish Case Research of Customized Warfarin Dosing.” In Proceedings of the twenty third USENIX Convention on Safety Symposium, 17–32. SEC’14. USA: USENIX Affiliation.
Wu, X., M. Fredrikson, S. Jha, and J. F. Naughton. 2016. “A Methodology for Formalizing Mannequin-Inversion Assaults.” In 2016 IEEE twenty ninth Laptop Safety Foundations Symposium (CSF), 355–70.
