An Empirical Analysis of Cancellable Transformations in a Behavioural Biometric Modality

An Empirical Analysis of Cancellable Transformations
in a Behavioural Biometric Modality
Marcelo Damasceno1;2 A.M.P. Canuto2
1Federal Institute of Education, Science and Technology of Rio Grande do Norte - São Gonçalo do
Amarante
2Federal University of Rio Grande do Norte
12/05/2013
Marcelo Damasceno (IFRN) An Empirical Analysis of Cancellable Transformations in a Behavioural B1i2o/m05e/2tr0ic13Modal1it/y35

About
This paper analyzes the performance of classification algorithms in the
context of cancellable behavioural biometrics, more specifically a
touch-screen dataset.
The main aim of this work is to analyse the gain that the use of
cancellable transformations can bring with respect to the behavioural
biometric context.

Outline
1 Introduction
2 Cancellable Transformations
3 TouchAnalytics
4 Experimental Analysis
5 Results
6 Conclusion and Further Work
7 References

Introduction
Outline
1 Introduction
3 TouchAnalytics
5 Results
7 References

Introduction
User Verification
Currently most computer systems use individual username and password
to authenticate their users [1];
Username-password method brings some problems as the use of same
username and password for different services on the Internet and the
stress to remember secure, long and complex passwords;

Introduction
Biometrics
Biometrics can be considered as the science of establishing the identity
of a person using his/her anatomical and/or behavioural traits.
Biometric traits have a number of desirable properties, such as reliability,
convenience, universality, and so forth.
Because of these characteristics, biometrics has been increasingly
developed over the last years.

Introduction
Behavioural Biometrics
Unlike physical biometrics, behavioural biometrics are related to user be-haviour/
actions [2].
These biometrics use behavioural patterns, such as gait, typing or the
way in which a user uses a computer system.
The behavioural biometrics is non-intrusive, i.e, often information
collection is not perceived by users.

Introduction
Biometrics Problems
The biometric is permanently associated with a user and cannot be
revoked or cancelled if compromised [3].
If a biometric identifier is compromised, it is lost forever and possibly the
same happens for every application where the biometric is used.
The use of cancellable biometrics is being increasingly adopted to
address such security issues.

Introduction
Cancellable Biometrics
This approach uses transformed or intentionally-distorted biometric data
instead of original biometric data for authentication [4, 5].
There is a risk that using such transformed data will decrease the
performance of the biometric-based system.

Cancellable Transformations
Outline
1 Introduction
3 TouchAnalytics
5 Results
7 References
Marcelo Damasceno (IFRN) An Empirical Analysis of Cancellable Transformations in a Behavioural1B2i/o0m5/2e0tr1ic3Moda1l0it/y35

Cancellable Transformation
The non-invertible transformation functions can transform the biometric
data in a way that it is computationally impossible to get the original form;
The distorted data brings some undesired consequences as high
variance, what makes more difficult the users identification;

Transformation Functions
1 Interpolation: Based on polynomial interpolations;
2 BioHashing: Characterized by transforming the original biometric into a
non-invertible binary sequence;
3 BioConvolving: The transformed functions are created through linear
combinations of sub-parts of the original biometric template;
4 DoubleSum: Consists of summing the attributes of the original biometric
model with two other attributes of the same sample;

TouchAnalytics
Outline
1 Introduction
3 TouchAnalytics
5 Results
7 References

TouchAnalytics
TouchAnalytics
The behavioural biometric modality used in this work is a touch screen
data, which represents a combination of strokes collected from
smartphones.
TouchAnalytics, was collected by Frank et al. [2]. They inform how the
data was collected, processed and some initial results

TouchAnalytics
TouchAnalytics
This dataset is composed of 30 attributes and all the attributes are
derived from the strokes obtained from 41 users.
Strokes are composed of horizontal and scrolling (vertical) movements.
The dataset was binarized because we use a verification process. It was
created a different dataset for each user.

TouchAnalytics
TouchAnalytics-Pre-Processing
As a result of the binarization transform, we have a huge number of
negative examples and few positives examples, featuring an imbalanced
dataset.
This problem was resolved using a lab-made tool that takes into
consideration the number of negative classes and the number of positive
examples.
T =
Np
Nnc
is the number of negatives instances that will be randomly selected in
each Nnc negative class. Where Np is the number of positive instances.
Thus, the number of negatives instances will be Nnc T .

TouchAnalytics
TouchAnalytics Scenarios
1 Inter Session: The goal is to authenticate users across multiple sessions
performed in the same day.
2 Inter Week: The goal is to authenticate users after in two different weeks
(the period of time between these two sessions is one week).
3 Intra Session: All the user data was used in the process, time
independently. In this scenario, we used a 10 fold cross-validation
process.

TouchAnalytics
TouchAnalytics Results
According to [6], the mean EER:
Intra Session are 0%: Within one session, most users do not
considerably change their touch behaviour;
Inter Session: 2% to 3%
Inter Week: 0% to 4%
This result indicates the behavioural biometrics (touch data) has good
perspectives in practical use.

Experimental Analysis
Outline
1 Introduction
3 TouchAnalytics
5 Results
7 References

Methodology I
The experiments will follow the same methodology applied by Frank et al. [6].
1 The raw dataset was downloaded
from: www.mariofrank.net/touchalytics.
2 The Inter Session dataset has the data recorded on 3 sessions at the same
day.
The Inter Week dataset consists of data record in two different weeks.
The Intra Session has data about all sessions, independent of time. The
classifiers were trained/tested using a 10-fold cross validation.
3 Each generated dataset was divided into scrolling and horizontal strokes
samples.
4 A binarized dataset was created for each user, aiming to be used in a
verification process.
5 The cancellable transformation functions (Interpolation, BioHashing,
BioConvolving, DoubleSum) were applied to each dataset to generate the
cancellable datasets;

Methodology II
6 After all these steps, the k-NN classifier using k=5 (5 was a chosen by
empiric tests) and SVM were used.
7 The mean EER obtained from each generated cancellable user dataset
was calculated.
8 The Mann-Whitney statistical test was applied to compare the results
obtained in the different cancellable datasets against original dataset
results. For this test, the confidence level is 95%(a = 0:05).

Results
Outline
1 Introduction
3 TouchAnalytics
5 Results
7 References

Results
Results and Discussion - Scrooling
Table: Mean Equal Error Rate - Scrooling Strokes
Session Orig. Inter. BioH. BioC. DS
k-NN
IS 1.36% 42.26% 31.44% 2.57% 9.61%
IW 1.14% 38.99% 32.48% 3.23% 9.72%
ITS 1.36% 9.43% 33% 3.60% 9.08%
SVM
IS 9.04% 41.86% 32.31% 1.85% 12.48%
IW 9.04% 39.41% 29.84% 3.11% 12.54%
ITS 9.04% 11.91% 29.08% 3.18% 11.80%
Shaded cells are statistically similar.
Bold values mean that the cancellable result was statistically better.

Results
Results and Discussion - Horizontal Strokes
Table: Equal Error Rate - Horizontal Strokes
Session Orig. Inter. BioH. BioC. DS
k-NN
IS 1.99% 42.26% 33.58% 0.258% 10.08%
IW 2.07% 49.70% 35.19% 0.253% 9.47%
ITS 1.99% 11.12% 34.10% 0.22% 9.87%
SVM
IS 17.43% 41.86% 38.82% 0.64% 21.56%
IW 17.43% 41.86% 41.02% 0.44% 22.45%
ITS 17.43% 24.77% 41.43% 0.52% 22.26%

Results
Discussion
From Tables 1 and 2, it can be concluded the BioConvolving and
DoubleSum have similar performance, when compared with the original
data.
BioConvolving dataset has four statistically similar results and six
statistically better results, in relation to the original dataset.
Double Sum results have five statistically similar results, out of 9 possible
cases.
The BioConvolving results in the horizontal strokes, Table 2. The use of
this transformation function brings statistically similar results using k-NN
and better statistically results using SVM.

Results
Results - Inter Session BoxPlots
(a) Scrooling Inter Session BoxPlots (b) Horizontal Inter Session BoxPlots
Figure: Inter Session BoxPlots

Results
Results - Intra Session BoxPlots
(a) Scrooling Intra Session BoxPlots (b) Horizontal Intra Session BoxPlots
Figure: Intra Session BoxPlots

Results
BoxPlot Discussion
The Original boxes has more outliers (points in plot) than BioConvolving
boxes;
The BioHashing boxes show that the results are very disperse, i.e, the
whiskeys are too long;
SVM boxes have longer whiskey than the k-NN boxes;
The Interpolation and BioHashing functions have the worst results.

Conclusion and Further Work
Outline
1 Introduction
3 TouchAnalytics
5 Results
7 References

Conclusion I
In this work, we performed an analysis of two well- known classifier using
cancellable behavioural biometrics.
Four cancellable functions (Interpo- lation, BioHashing, BioConvolving
and Double Sum) were applied in this dataset to demonstrate the
importance and perspectives of cancellable behavioural biometrics.
The k-NN and SVM classifiers have interesting results in BioConvolving
and Double Sum datasets. The mean ERR was between 0:22% and
3:60% in BioConvolving datasets and the mean Double Sum EER was
between 9:08% and 22:45%.
The Interpolation and BioHashing functions needs more refinements to
minimize the Equal Error Rate as parameter optmization, ensemble
methods and multimodal biometrics processing.

Conclusion II
As a future work, in order to improve the results we will use more
classifiers as MultiLayer Percep- trons, optimize cancelable function
parameters or even use ensemble methods.

References
Outline
1 Introduction
3 TouchAnalytics
5 Results
7 References

References
References I
W. Jackson, “Antisec hackers claim theft of military e-mails from booz
allen,” Internet, Julho 2011, acessado em Novembro de 2011. [Online].
Available: http://gcn.com/articles/2011/07/11/
antisec-booz-allen-hack-military-emails.aspx
K. Revett, Behavioral Biometrics: a Remote Access Approach. John
Wiley Sons, Ltd, 2008.
A. K. Jain, K. Nandakumar, and A. Nagar, “Biometric template security,” in
EURASIP Journal On Advances in Signal Processing, 2008.
C. Lee and J. Kim, “Cancelable fingerprint templates using
minutiae-based bit-strings,” Journal of Network and Computer
Applications, vol. 33, no. 3, pp. 236 – 246, 2010.

References
References II
A. Nagar, K. Nandakumar, and A. K. Jain, “A hybrid biometric
cryptosystem for securing fingerprint minutiae templates,” Pattern Recogn.
Lett., vol. 31, pp. 733–741, June 2010.
M. Frank, R. Biedert, E. Ma, I. Martinovic, and D. Song, “Touchalytics: On
the Applicability of Touchscreen Input as a Behavioral Biometric for
Continuous Authentication,” in IEEE Transactions on Information
Forensics and Security, vol. 8, no. 1, 2013, pp. 136–148. [Online].
Available: http://www.mariofrank.net/touchalytics/

References
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