Hi everyone, I'm sicong
Today, I'm going to talk about the privacy adversarial network
It's about how to learn the representations for mobile data privacy
This is joint work with Rice University
Today, Machine learning has benefited a lot of mobile services
Such as the Siri, and Amazon
And the mobile users are always required to
Upload the voice, image, vedio, and other raw data
For intelligent services
But sending the raw data to mobile service providers
presents serious challenges to user privacy
For example, the mobile users
say "Hi siri"
There is a risk of disclosing the voice privacy
Towards addressing this challenge
Our key insight is that most services
acturally do not need the raw data
Our goal is to avoid disclosing the raw data
to service providers
by creating a device-local intermediate component
that encodes the raw data
and only sends the encoded data
So in this way,
the encoded data must be useful and private
Um， However, existing solutons struggle to balance
the seemingly conflicting utility and privacy objectives
such as the widely practiced Noisy data
Task-specific feature
and Hybrid DNN feature
Unlike the previous work, we want to
automatically learn the deep features
to find a sweet tradeoff
between the utility and privacy
In this work,
we want to maxmize the utility
this is quantified by the task inference accuracy
and maxmize the reconstruction error
it's used to quantify the agnostic privacy
and minimize the inference accuracy
of some private attributes
that is used to measure the
user-specified privacy
But, when we use the CNN to learn features
by maximizing the task inference accuracy
the reconstruction error are always small
And, when the AutoEncoder learns features
by minimizing the reconstruction error
which conflicts with our objective
So, we focus on the training of the Encoder
by introducing a new deep mode: the PAN
it employs a utility discriminator
as a positive teacher
to supervise the training of the Encoder
and employs the adversaries
to confront with the training of the Encoder
We hope a powerful Encoder must come from
a tough teacher,
and two powerful adversaries
so, we present a new training algorithm of PAN
to avoid the model degradation
Using the digit recognition,
image classification,
sound sensing, human activity prediction
and driver behavior recognition,
we evaluate the performance of PAN
We train two types of PAN
The PAN_1 is trained with one attacker
and the PAN_2 is trained with two attackers
And we compare the PAN's performance
with the other four baselines
We can see that the PAN can achieve
the best utility-privacy tradeoff
And PAN_2 also achieved the best tradeoff
And also, the utility of the PAN's Encoder output
is even better than that of the standard DNN
We further visualize the PAN
to seek, how, more insights,
how does the PAN
disentangle the feature components relevant to privacy
from those relevant to utility
We can see that,
the PAN pushes features
towards a more constrictive space
and the space is dominated by
the samples without redundant,
private, sensitive background information
And we also visualize the PAN's Encoder output
on the reconstruction privacy
And, finally, we also provide
a manifold learing-based interpretation
for better utility and privacy performance
of PAN's Encoder output
Emm, we think PAN is a framework
it can further accomodate
more choices of the utility and privacy quantification
and intgrate more
utility discriminators
and privacy attackers
And, also, more insights
to guarantee and accelerate
the training of convergence of PAN are needed
Thank you !