Analysis of iris obfuscation
Generalising eye information processes for privacy studies in eye tracking
Eye-tracking based research has shown that human eyes reveal insights
into both physical and mental properties of their owners.
We use the term eye-information to refer to these properties. Because of
the increased sharing of data and prevalence of eye-tracking data, knowledge of exactly what is
shared and development of methods for preventing information leakage is increasingly important.
This work concerns selective removal of sensitive information ien eye-tracking data.
It presents a framework for describing eye information security and analyses specific methods
for preventing iris-pattern recognition.
The presented research is characterised by
three distinct contributions to the field of eye tracking.
The first is a framework for understanding how
information flows in eye-tracking systems and how different eye-information sources interact.
The second, is a set methods for preventing iris recognition
while retaining utility for gaze estimation.
The third is a methodology for analysing selective security methods.
An eye-tracker is a combination of hardware and software that implements some
application. The application may run locally or remotely and may expose external user
endpoints. The system captures information through digital encoding of eye information.
There are two approaches to eye information security. The first approach is to focus on
the user endpoint of a larger system to prevent information leaks through the application itself.
We call this outwardly directed security, because it focuses on external risks.
The other approach is to view all data in the pipeline as sensitive because it contains
possibly sensitive information that is not necessarily needed for the current application.
We call this internally directed security.
Eye information framework
An eye-tracker is a signal processing system.
Eye-information and other signals are encoded through the image-capturing process into an
image signal which is then further processed to recreate the original eye properties.
The image thus contains encoded sensitive information
which should be separated from other signal sources.
For this, we introduce the concepts of obfuscation and utility.
Let us focus on just the iris and gaze properties for now.
Obfuscation is the process of hiding or removing information from unstructured
data. This is at odds with the usefulness of the data for some predefined application.
This usefulness is named utility.
Fundamentally then, internally directed security methods need to be aware of both metrics.
Information theory describes how obfuscation and utility
are related. The entropy of each signal is a measure of the information it contains.
Because the signals are dependent, there will be entropy in the system shared by multiple signals.
This is mutual information. The entropy that is left
is conditional entropy, which is parellel to the concept of conditional probability.
In this view, obfuscation is simply a minimisation of mutual information and a
maximisation of conditional entropy between a property and the image.
Utility is then the exact opposite.
The experimental part of our work is motivated by a current lack of detailed
iris obfuscation analyses. The iris pattern is a very stable biometric used for large-scale
applications of personal identification. Since the capturing conditions are similar to that of
head-mounted eye-tracking, iris recognition can be performed on eye-tracking data.
Current research has tested optical defocus and a noise-generation method for obfuscating
the iris. We contribute methods that are better suited to selective obfuscation
by utilising the information relationship revealed by the information framework.
Our approaches assume iris patterns to be primarily defined by high-frequency signal
components and gaze information to be primarily defined by edges and low-frequency components.
We consider three method categories.
Filter-based methods try to remove iris-pattern information while retaining edges.
Noise-based methods have two actions. First, they try to confuse
the iris-recognition algorithm by adding random high-frequency data.
Second, they overwrite existing information. The noise is drawn for each pixel individually.
Finally, we combine the two base approaches. We combine
the bilateral filter method and cauchy noise method.
Evaluation
We introduce a methodology for testing the practical security of
obfuscation methods for iris recognition.
Although we consider multiple attack strategies in the paper,
here we focus on just the worst case, where the attacker has access to a
labelled dataset and has the ability to perform the obfuscation function
on all the samples. This makes it possible to estimate the iris code distance distributions
and select an optimal threshold. Because the distributions are assumed to overlap significantly
(due to the obfuscation function), the ideal threshold for an attacker is just the one that
maximises recall but requires some minimum level of precision. Requiring precision=1 provides a
lower bound on the fraction of images that can be correctly identified despite the obfuscation.
Our results are promising for the two methods that combine bilateral filtering and cauchy noise
addition. At relatively low gaze errors they vastly outperform all other methods.
Equally important however, is that this graph proves how easy it is to get a false
sense of security if the probabilistic nature of the problem is not adequately considered.
Sadly, due to the very low ratio of true positives to to true negatives,
the metric precision is limited to approximately 1 in 17.000.
Conclusion
In this presentation, we have laid out our argument for the importance of considering
internally directed security in eye-tracking.
It is not enough to protect processed data through encryption or differential privacy
because the raw tracking data itself may contain sensitive information.
We have provided a more coherent view of internally directed security in eye-tracking
by presenting a framework for modelling security in terms of information
in a communication system and presenting a methodology for evaluating perceived security.
Finally, we have presented obfuscation methods that significantly improve
on earlier research while demonstrating that security measures can potentially be deceiving
given enough attacker resources.