Differential PrivacyStudy Group March 2017

Abadi, M., Chu, A., Goodfellow, I., McMahan, H.B., Mironov, I., Talwar, K. and Zhang, L., 2016, October. Deep learning with differential privacy. In Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security (pp. 308-318). ACM.

Deep Learning with Differential Privacy

MotivationAbadi, M. et al. Deep learning with differential privacy. 2016

Fredrikson, M., Jha, S. and Ristenpart, T., 2015, October. Model inversion attacks that exploit confidence information and basic countermeasures. In Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security (pp. 1322-1333). ACM.

ε,𝛿

ε,𝛿

Theorem 1: Abadi, M. et al. Deep learning with differential privacy. 2016

Algorithm

Algorithm 1: Abadi, M. et al. Deep learning with differential privacy. 2016

N

Algorithm 1: Abadi, M. et al. Deep learning with differential privacy. 2016

Algorithm 1: Abadi, M. et al. Deep learning with differential privacy. 2016

N

Algorithm 1: Abadi, M. et al. Deep learning with differential privacy. 2016

N

gt(x3)gt(x3)gt(x8)gt(x11)

Algorithm 1: Abadi, M. et al. Deep learning with differential privacy. 2016

N

gt(x3)gt(x3)gt(x8)gt(x11)

clipping also seen in normal SGD for non-privacy reasons — but then done on the batch level

Algorithm 1: Abadi, M. et al. Deep learning with differential privacy. 2016

N

gt(x3)gt(x3)gt(x8)gt(x11)

Algorithm 1: Abadi, M. et al. Deep learning with differential privacy. 2016

N

lot

batch

Algorithm 1: Abadi, M. et al. Deep learning with differential privacy. 2016

N

lotbatch

Implementation

class DPSGD_Optimizer(): def __init__(self, accountant, sanitizer): self._accountant = accountant self._sanitizer = sanitizer

def Minimize(self, loss, params, batch_size, noise_options): # Accumulate privacy spending before computing # and using the gradients. priv_accum_op = self._accountant.AccumulatePrivacySpending( batch_size, noise_options) with tf.control_dependencies(priv_accum_op): # Compute per example gradients px_grads = per_example_gradients(loss, params) # Sanitize gradients sanitized_grads = self._sanitizer.Sanitize( px_grads, noise_options) # Take a gradient descent step return apply_gradients(params, sanitized_grads)

def DPTrain(loss, params, batch_size, noise_options): accountant = PrivacyAccountant() sanitizer = Sanitizer() dp_opt = DPSGD_Optimizer(accountant, sanitizer) sgd_op = dp_opt.Minimize( loss, params, batch_size, noise_options) eps, delta = (0, 0) # Carry out the training as long as the privacy # is within the pre-set limit. while within_limit(eps, delta): sgd_op.run() eps, delta = accountant.GetSpentPrivacy()

Figure 1: Abadi, M. et al. Deep learning with differential privacy. 2016

class DPSGD_Optimizer(): def __init__(self, accountant, sanitizer): self._accountant = accountant self._sanitizer = sanitizer

def Minimize(self, loss, params, batch_size, noise_options): # Accumulate privacy spending before computing # and using the gradients. priv_accum_op = self._accountant.AccumulatePrivacySpending( batch_size, noise_options) with tf.control_dependencies(priv_accum_op): # Compute per example gradients px_grads = per_example_gradients(loss, params) # Sanitize gradients sanitized_grads = self._sanitizer.Sanitize( px_grads, noise_options) # Take a gradient descent step return apply_gradients(params, sanitized_grads)

def DPTrain(loss, params, batch_size, noise_options): accountant = PrivacyAccountant() sanitizer = Sanitizer() dp_opt = DPSGD_Optimizer(accountant, sanitizer) sgd_op = dp_opt.Minimize( loss, params, batch_size, noise_options) eps, delta = (0, 0) # Carry out the training as long as the privacy # is within the pre-set limit. while within_limit(eps, delta): sgd_op.run() eps, delta = accountant.GetSpentPrivacy()

Figure 1: Abadi, M. et al. Deep learning with differential privacy. 2016

class DPSGD_Optimizer(): def __init__(self, accountant, sanitizer): self._accountant = accountant self._sanitizer = sanitizer

def Minimize(self, loss, params, batch_size, noise_options): # Accumulate privacy spending before computing # and using the gradients. priv_accum_op = self._accountant.AccumulatePrivacySpending( batch_size, noise_options) with tf.control_dependencies(priv_accum_op): # Compute per example gradients px_grads = per_example_gradients(loss, params) # Sanitize gradients sanitized_grads = self._sanitizer.Sanitize( px_grads, noise_options) # Take a gradient descent step return apply_gradients(params, sanitized_grads)

def DPTrain(loss, params, batch_size, noise_options): accountant = PrivacyAccountant() sanitizer = Sanitizer() dp_opt = DPSGD_Optimizer(accountant, sanitizer) sgd_op = dp_opt.Minimize( loss, params, batch_size, noise_options) eps, delta = (0, 0) # Carry out the training as long as the privacy # is within the pre-set limit. while within_limit(eps, delta): sgd_op.run() eps, delta = accountant.GetSpentPrivacy()

Figure 1: Abadi, M. et al. Deep learning with differential privacy. 2016

class DPSGD_Optimizer(): def __init__(self, accountant, sanitizer): self._accountant = accountant self._sanitizer = sanitizer

def Minimize(self, loss, params, batch_size, noise_options): # Accumulate privacy spending before computing # and using the gradients. priv_accum_op = self._accountant.AccumulatePrivacySpending( batch_size, noise_options) with tf.control_dependencies(priv_accum_op): # Compute per example gradients px_grads = per_example_gradients(loss, params) # Sanitize gradients sanitized_grads = self._sanitizer.Sanitize( px_grads, noise_options) # Take a gradient descent step return apply_gradients(params, sanitized_grads)

def DPTrain(loss, params, batch_size, noise_options): accountant = PrivacyAccountant() sanitizer = Sanitizer() dp_opt = DPSGD_Optimizer(accountant, sanitizer) sgd_op = dp_opt.Minimize( loss, params, batch_size, noise_options) eps, delta = (0, 0) # Carry out the training as long as the privacy # is within the pre-set limit. while within_limit(eps, delta): sgd_op.run() eps, delta = accountant.GetSpentPrivacy()

Figure 1: Abadi, M. et al. Deep learning with differential privacy. 2016

class DPSGD_Optimizer(): def __init__(self, accountant, sanitizer): self._accountant = accountant self._sanitizer = sanitizer

def Minimize(self, loss, params, batch_size, noise_options): # Accumulate privacy spending before computing # and using the gradients. priv_accum_op = self._accountant.AccumulatePrivacySpending( batch_size, noise_options) with tf.control_dependencies(priv_accum_op): # Compute per example gradients px_grads = per_example_gradients(loss, params) # Sanitize gradients sanitized_grads = self._sanitizer.Sanitize( px_grads, noise_options) # Take a gradient descent step return apply_gradients(params, sanitized_grads)

def DPTrain(loss, params, batch_size, noise_options): accountant = PrivacyAccountant() sanitizer = Sanitizer() dp_opt = DPSGD_Optimizer(accountant, sanitizer) sgd_op = dp_opt.Minimize( loss, params, batch_size, noise_options) eps, delta = (0, 0) # Carry out the training as long as the privacy # is within the pre-set limit. while within_limit(eps, delta): sgd_op.run() eps, delta = accountant.GetSpentPrivacy()

Figure 1: Abadi, M. et al. Deep learning with differential privacy. 2016

class DPSGD_Optimizer(): def __init__(self, accountant, sanitizer): self._accountant = accountant self._sanitizer = sanitizer

def Minimize(self, loss, params, batch_size, noise_options): # Accumulate privacy spending before computing # and using the gradients. priv_accum_op = self._accountant.AccumulatePrivacySpending( batch_size, noise_options) with tf.control_dependencies(priv_accum_op): # Compute per example gradients px_grads = per_example_gradients(loss, params) # Sanitize gradients sanitized_grads = self._sanitizer.Sanitize( px_grads, noise_options) # Take a gradient descent step return apply_gradients(params, sanitized_grads)

def DPTrain(loss, params, batch_size, noise_options): accountant = PrivacyAccountant() sanitizer = Sanitizer() dp_opt = DPSGD_Optimizer(accountant, sanitizer) sgd_op = dp_opt.Minimize( loss, params, batch_size, noise_options) eps, delta = (0, 0) # Carry out the training as long as the privacy # is within the pre-set limit. while within_limit(eps, delta): sgd_op.run() eps, delta = accountant.GetSpentPrivacy()

Figure 1: Abadi, M. et al. Deep learning with differential privacy. 2016

Code on GitHub: • More logic for dealing with batches • Two accountants:

• AmortizedAccountant • GaussianMomentsAccountant

• Per example gradient code (including for convolutional layers)

• MNIST example

Also has code for Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data https://github.com/tensorflow/models/tree/master/differential_privacy

Results

Figure 2: Abadi, M. et al. Deep learning with differential privacy. 2016

Figure 3: Abadi, M. et al. Deep learning with differential privacy. 2016

Figure 3: Abadi, M. et al. Deep learning with differential privacy. 2016

“compare to 98.3% without

Figure 3: Abadi, M. et al. Deep learning with differential privacy. 2016

Figure 4: Abadi, M. et al. Deep learning with differential privacy. 2016

Figure 5: Abadi, M. et al. Deep learning with differential privacy. 2016

86% Accuracy

80 % accuracy

retrain

fix from cifar-100

Figure 6: Abadi, M. et al. Deep learning with differential privacy. 2016

Hall, R., Rinaldo, A. and Wasserman, L., 2013. Differential privacy for functions and functional data. Journal of Machine Learning Research, 14(Feb), pp.703-727.

Differential Privacy for Functions and Functional Data

Motivation

1. Useful if results naturally function valued

2. May want to have a data summary that is a function

1. Useful if results naturally function valued

2. May want to have a data summary that is a function

1. Useful if results naturally function valued

2. May want to have a data summary that is a function

x xx x x xx

Proofs

Hall, R., et. al. Differential privacy for functions and functional data. 2016

Hall, R., et. al. Differential privacy for functions and functional data. 2016

Skip rest of proof of Prop3

Hall, R., et. al. Differential privacy for functions and functional data. 2016

Hall, R., et. al. Differential privacy for functions and functional data. 2016

Hall, R., et. al. Differential privacy for functions and functional data. 2016

Hall, R., et. al. Differential privacy for functions and functional data. 2016

Hall, R., et. al. Differential privacy for functions and functional data. 2016

Hall, R., et. al. Differential privacy for functions and functional data. 2016

Hall, R., et. al. Differential privacy for functions and functional data. 2016

Hall, R., et. al. Differential privacy for functions and functional data. 2016

Hall, R., et. al. Differential privacy for functions and functional data. 2016

Applied to KDEs

Hall, R., et. al. Differential privacy for functions and functional data. 2016

Hall, R., et. al. Differential privacy for functions and functional data. 2016

Hall, R., et. al. Differential privacy for functions and functional data. 2016

Hall, R., et. al. Differential privacy for functions and functional data. 2016

Hall, R., et. al. Differential privacy for functions and functional data. 2016

Hall, R., et. al. Differential privacy for functions and functional data. 2016

Hall, R., et. al. Differential privacy for functions and functional data. 2016

Hall, R., et. al. Differential privacy for functions and functional data. 2016

Hall, R., et. al. Differential privacy for functions and functional data. 2016

Hall, R., et. al. Differential privacy for functions and functional data. 2016

varying alpha

varying alpha

varying beta

varying beta

varying beta

demo maybe…

https://gist.github.com/john-bradshaw/e63d2a20537beda035b32224a1be8831

references

Abadi, M., Chu, A., Goodfellow, I., McMahan, H.B., Mironov, I., Talwar, K. and Zhang, L., 2016, October. Deep learning with differential privacy. In Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security (pp. 308-318). ACM.

Hall, R., Rinaldo, A. and Wasserman, L., 2013. Differential privacy for functions and functional data. Journal of Machine Learning Research, 14(Feb), pp.703-727.

appendix