Hi, everyone.
I am Yuezhong Wu from University of New South Wales.
Today, I’m going to present my work that is Auto-key: using autoencoder to speed up
gait-based key generation in body area networks.
Recent research shows the potential of wearable devices to generate shared secret key by exploiting
the gait biometric.
In figure 1, two devices measure the gait with accelerometers and quantize gait signal
to binary bits we called key.
When the two keys are the same, they can secure their communication.
But in real scenario, the independent observations of the same gait biometric from different
body locations do not match perfectly.
In Fig 2, we can see there are dissimilarities between gait signals from different locations.
After quantization, the dissimilarity will introduce many mismatched bits and results
in unsuccessful pairing and slow down the key generation.
So, the motivation of our work is to remove the noise and improve the similarity of observations
from different body locations and consequently, to reduce the mismatched bits.
The main contributions of our work are using autoencoder to predict the gait signal of
one body location by using gait signal from other body locations.
By using the prediction to do key generation, we speed up the key generation by a factor
of 1.9.
We also proposed a transfer learning framework to reduce training data and time.
To remove the noise and improve the similarity, we use a neural network called denoising autoencoder.
We consider the gait signal from chest as the ground truth.
and the gait signals from other body locations are the corrupted gait signal by the noise
caused from other body parts’ motions like leg and arm.
When training, the autoencoder take the corrupted version as input.
By solving the loss function to minimize the mean square error between the output and ground
truth.
The autoencoder will learn to discard the noise and generate a prediction of the ground
truth.
When we have trained model, we add a prediction step before quantization.
Like in Figure 3, device at other body locations needs to input its gait signal to the trained
denoising autoencoder to get a predicted ground truth and use the prediction to generate keys.
Since training is an overhead.
We proposed a two-tier transfer learning framework to reduce the training data and training time.
A universal model is trained in the first tier called cloud training.
The user re-train the universal model using his own data and device in the second tier
called edge training.
We use a dataset of 15 subjects to do the evaluation.
We train models that use acceleration observed at other body locations to predict gait ground
truth at chest.
Here we can see the signals.
The blue curves are the ground truth from chest.
The green curves are the corrupted version from other body locations, the red curves are the
predicted ground truth by the green curves. We can see the red and blue curves are much
more similar with each other.
Then we do the key generation.
We use two measurements.
Bit agreement rate represents the percentage of the bits matching in the keys.
The key generation rate represents the number of keys generated per minute.
We can see in Figure 5.
Compared to using the corrupted signal, using predicted ground truth significantly improve
the bit agreement rate and key generation rate.
We also collect an in-house dataset of 6 subjects to evaluate the transfer learning framework,
we can see in the table.
The baseline is training a model from scratch using all data.
Compared to baseline, transfer learning can save 50% of the training data and 88% of the training
time.
We also collect data and do a security analysis of three attacks.
Passive attack, mimicking attack and video side-channel attack.
In figure 5, we can see all the attacks can only reach around 62% of bit agreement rate that
is much lower than the legitimate devices.
And in the whole security analysis, all attackers fail to produce any successful key-pairing
with legitimate devices.
Thank you for listening.
I would like to take any question.