Hello everyone. My name is Qi Lin.
I am from University of New South Wales.
I am giving this talk on our paper of IMWUT Volume 4 Issue 1 Article 41.
It is about our key generation system for body area network.
Let's start with the background.
In multi-wearable scenarios, wearable devices may need to frequently exchange highly private sensory information.
In order to protect sensitive information, legitimate devices must establish initial trust relationship.
Traditional approaches are based on pre-deployed secret, however they are not scalable with growing number of wearable devices.
In the area of BAN security, exploiting biometrics as shared secret is one of the most popular direction for key generation system.
Biometrics are inherent, unique, and accessible for wearable devices, and usually they are hard to obtain or spoof for malicious devices.
Currently, popular biometric choices include heartbeat, gait, etc.
Though previous works demonstrated feasibility of biometric-based key generation system,
the power consumed by high-frequency accelerometer sampling is considered significant for resource constraint wearables.
While harvesting kinetic energy from human activities drawing lots of research attentions,
previous studies also showed that KEH can be used as an energy-efficient alternative for accelerometers.
However, due to the high level of noise inherent in KEH signal,
the key generation system needs to tackle more bit mismatches.
Under this background, we propose KEHKey system.
KEHKey system exploits the gait shape captured by KEH as the shared secret.
Suppose Alice and Bob are two KEH-embedded wearable devices worn on a user.
At first, Alice and Bob sample the user's gait signal independently and simultaneously.
Then, they quantize the samples into bit streams.
Finally, Alice and Bob apply an information reconciliation approach to correct mismatches to agree on the same final key.
We built four prototype devices by instrumenting a data logger, i.e. SensorTags from Texas Instruments
with a KEH, i.e. PPA 1001 piezoelectric cantilevers produced by MIDE Technology.
We collected data from 24 participants.
All participants wore four prototypes, on their chest, waist, head and hands,
and they were asked to walked indoor at their normal speed.
Accurate time synchronization of two devices is needed,
as even a small temporal offset can lead to numbers of mismatches.
On receiving the communication request or response,
two wearable devices synchronize their clocks by observing the next heel strike,
which can be detected accurately during each step.
When KEHKey attempts to collect data from user's hand or user's arm,
the energy generated from the limb swing will be added to the total KEH signal, making it difficult for key pairing.
It is clear that the step frequency on torso is as twice as that on the arm.
Therefore, we can filter out limb swing components with a band pass filter.
Figures below show that effect of a simple band pass filter.
We used a conventional 2-ary guard band-based quantization method to convert gait shape into bit streams.
Each sample is quantized to be '0', '1' or discarded.
In symmetric key generation system, two legitimate parties generate their keys independently.
Two keys are usually similar, but not the same.
To correct mismatches, we implemented state-of-the-art compressive sensing-based information reconciliation approach.
In CS-based information reconciliation protocol, one pairing device transmits its compressed key via public channel,
so that its pairing party can correct mismatches based on the compressed key.
Dimension of compressed key is the balancing factor of security and effectiveness.
If the compressed key is too short, legitimate parties cannot correct all mismatches.
If it is too long, it will leak too much information to attackers so that attackers can derive the secret.
We selected dimension based on collected data to ensure effectiveness and security.
After tuning all parameters of our system, our results show that KEHKey can generate the same key for two legitimate devices at a speed of 12.6 bps.
It can also reduce energy consumption by 59% compared to accelerometer-based methods.
To analyze security of the key generation system, we also developed three typical adversarial attacks:
passive eavesdropping attack, active mimicking attack, and video side channel attack.
Our results shows that they can achieve 74% of bit agreement rate at the highest.
As key generation system requires bit-for-bit equal key, they have 0 success rate.
And that concludes this talk. Thank you.