an empirical analysis of cancellable transformations in a behavioural biometric modality

An Empirical Analysis of Cancellable Transformationsin 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 doAmarante

2Federal University of Rio Grande do Norte

12/05/2013

Marcelo Damasceno (IFRN) An Empirical Analysis of Cancellable Transformations in a Behavioural Biometric Modality12/05/2013 1 / 35

About

This paper analyzes the performance of classification algorithms in thecontext of cancellable behavioural biometrics, more specifically atouch-screen dataset.

The main aim of this work is to analyse the gain that the use ofcancellable transformations can bring with respect to the behaviouralbiometric 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 passwordto authenticate their users [1];

Username-password method brings some problems as the use of sameusername and password for different services on the Internet and thestress to remember secure, long and complex passwords;


Introduction

Biometrics

Biometrics can be considered as the science of establishing the identityof 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 increasinglydeveloped 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 theway in which a user uses a computer system.

The behavioural biometrics is non-intrusive, i.e, often informationcollection is not perceived by users.


Introduction

Biometrics Problems

The biometric is permanently associated with a user and cannot berevoked or cancelled if compromised [3].

If a biometric identifier is compromised, it is lost forever and possibly thesame happens for every application where the biometric is used.

The use of cancellable biometrics is being increasingly adopted toaddress such security issues.


Introduction

Cancellable Biometrics

This approach uses transformed or intentionally-distorted biometric datainstead of original biometric data for authentication [4, 5].

There is a risk that using such transformed data will decrease theperformance of the biometric-based system.


Cancellable Transformations

Outline

1 Introduction


3 TouchAnalytics


5 Results


7 References



Cancellable Transformation

The non-invertible transformation functions can transform the biometricdata in a way that it is computationally impossible to get the original form;

The distorted data brings some undesired consequences as highvariance, 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 screendata, which represents a combination of strokes collected fromsmartphones.

TouchAnalytics, was collected by Frank et al. [2]. They inform how thedata was collected, processed and some initial results


TouchAnalytics

TouchAnalytics

This dataset is composed of 30 attributes and all the attributes arederived 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 wascreated a different dataset for each user.


TouchAnalytics

TouchAnalytics-Pre-Processing

As a result of the binarization transform, we have a huge number ofnegative examples and few positives examples, featuring an imbalanceddataset.

This problem was resolved using a lab-made tool that takes intoconsideration the number of negative classes and the number of positiveexamples.

T =Np

Nnc

is the number of negatives instances that will be randomly selected ineach 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 sessionsperformed 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, timeindependently. In this scenario, we used a 10 fold cross-validationprocess.


TouchAnalytics

TouchAnalytics Results

According to [6], the mean EER:

Intra Session are 0%: Within one session, most users do notconsiderably change their touch behaviour;

Inter Session: 2% to 3%

Inter Week: 0% to 4%

This result indicates the behavioural biometrics (touch data) has goodperspectives 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. Theclassifiers were trained/tested using a 10-fold cross validation.

3 Each generated dataset was divided into scrolling and horizontal strokessamples.

4 A binarized dataset was created for each user, aiming to be used in averification process.

5 The cancellable transformation functions (Interpolation, BioHashing,BioConvolving, DoubleSum) were applied to each dataset to generate thecancellable datasets;


www.mariofrank.net/touchalytics


Methodology II

6 After all these steps, the k -NN classifier using k=5 (5 was a chosen byempiric tests) and SVM were used.

7 The mean EER obtained from each generated cancellable user datasetwas calculated.

8 The Mann-Whitney statistical test was applied to compare the resultsobtained in the different cancellable datasets against original datasetresults. For this test, the confidence level is 95%(α = 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 -NNIS 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%

SVMIS 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 -NNIS 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%

SVMIS 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 andDoubleSum have similar performance, when compared with the originaldata.

BioConvolving dataset has four statistically similar results and sixstatistically better results, in relation to the original dataset.

Double Sum results have five statistically similar results, out of 9 possiblecases.

The BioConvolving results in the horizontal strokes, Table 2. The use ofthis transformation function brings statistically similar results using k -NNand 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 BioConvolvingboxes;

The BioHashing boxes show that the results are very disperse, i.e, thewhiskeys 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 usingcancellable behavioural biometrics.

Four cancellable functions (Interpo- lation, BioHashing, BioConvolvingand Double Sum) were applied in this dataset to demonstrate theimportance and perspectives of cancellable behavioural biometrics.

The k-NN and SVM classifiers have interesting results in BioConvolvingand Double Sum datasets. The mean ERR was between 0.22% and3.60% in BioConvolving datasets and the mean Double Sum EER wasbetween 9.08% and 22.45%.

The Interpolation and BioHashing functions needs more refinements tominimize the Equal Error Rate as parameter optmization, ensemblemethods and multimodal biometrics processing.



Conclusion II

As a future work, in order to improve the results we will use moreclassifiers as MultiLayer Percep- trons, optimize cancelable functionparameters 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 boozallen,” 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. JohnWiley & Sons, Ltd, 2008.

A. K. Jain, K. Nandakumar, and A. Nagar, “Biometric template security,” inEURASIP Journal On Advances in Signal Processing, 2008.

C. Lee and J. Kim, “Cancelable fingerprint templates usingminutiae-based bit-strings,” Journal of Network and ComputerApplications, vol. 33, no. 3, pp. 236 – 246, 2010.


http://gcn.com/articles/2011/07/11/antisec-booz-allen-hack-military-emails.aspx

http://gcn.com/articles/2011/07/11/antisec-booz-allen-hack-military-emails.aspx

References

References II

A. Nagar, K. Nandakumar, and A. K. Jain, “A hybrid biometriccryptosystem 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: Onthe Applicability of Touchscreen Input as a Behavioral Biometric forContinuous Authentication,” in IEEE Transactions on InformationForensics and Security, vol. 8, no. 1, 2013, pp. 136–148. [Online].Available: http://www.mariofrank.net/touchalytics/


http://www.mariofrank.net/touchalytics/

References

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an empirical analysis of cancellable transformations in a behavioural biometric modality

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