Your heartbeat could be your password

Department of Electrical EngineeringNational Chung Hsing UniversityTaichung, Taiwan, R.O.C. Ching-Kun ChenApril 30, 2013

Outline Introduction Chaos and Quantifying Chaotic Behavior Phase space reconstruction Lyapunov Exponent Chaotic Functions ECG-Based Biometric Recognition Synchronization of Two Identical Lorenz Systems System Design and Secure Information Transmission Experimental Results and Discussion Conclusions and Feature Work

Motivation

As multimedia and network technologies continue to develop, digital information is increasingly applied in real-world applications. However, digitized information is easy to copy, making information security increasingly crucial in the communication process. Cryptography is one of basic methodologies for information security. Since 1990s, many researchers have noticed that there exists a close relationship between chaos and cryptography. Recently, chaos theory gradually plays an active role in cryptography consistently.

Introduction

Motivation

Biometric based authentication system provides better security solutions than conventional. But some biological parameters that are used as biometric dont provide the robustness against falsified credentials such as voice can be copied through microphone, fingerprint can be collected on silicon surface and iris can be copied on contact lenses. ECG doesnt have these problems and its unique in every individual.

Human heart is a supremely complex biological system. There are no model that account for all of cardiac electrical activity. Researchers in the field of chaotic dynamical system theory have used several features, including correlation dimension (D2), Lyapunov exponents ( ), approximate entropy, etc. These key features have can explain ECGs behavior for diagnostic purposes. Introduction

ChaosECGEncryptionTransmissionChaos-related researchLiterature Survey

Literature Survey - Chaotic Encryption Pareek et al. proposed an image encryption scheme which utilizes two chaotic logistic maps and an external key of 80 bits. (2006)

Kwok et al. proposed a fast chaos-based image cryptosystem with the architecture of a stream cipher. (2007)

Behina et al. proposed a novel algorithm for image encryption based on mixture of chaotic maps, using one dimensional chaotic map and their coupling to obtain high level security. (2008)

Zhu et al. proposed a chaos-based symmetric image encryption scheme using a bit-level permutation. (2012)

Literature Survey - Chaotic ECG Signal Babloyantz et al. showed the human heart is not a simple oscillator, the heart behavior exhibits a chaotic behavior. (1988)

Casaleggio et al. applied lyapunov exponents to analysis and estimation of ECG signals from MIT-BIH database. (1995, 1997)

Owis et al. present a study of features based on the nonlinear dynamical ECG signals for arrhythmia detection and classification. (2002)

Al-Fahoum et al. used simple reconstructed phase space approach for ECG arrhythmia classification. (2006)

Literature Survey - Chaotic Transmission The application of chaotic synchronization to secret communication was suggested by Pecora and Carroll. (1990, 1991)

A successful experimental realization of signal masking and recovery was first made using electric circuits by Cuomo. (1993)

Control of chaos techniques have also been used for the transmission of messages by means of chaotic signals. There are several control techniques used to synchronize chaotic systems, such as fuzzy control, delayed neural networks, impulsive control, and linear error feedback control. The chaotic signals can be used to mask information waveforms or serve as modulating waveforms.

Chaos and Quantifying Chaotic Behavior

Phase space reconstructionDescription of MethodsPackard et al. (1980)proposed phase space reconstruction that is a standard procedure while analyzing chaotic systems which shows the trajectory of the system in time. Phase space in d dimensions display a number of points of the system, where each point is given by

where n is the moment in time of a system variable, with denoting the sampling period and T being the period between two consecutive measurement for constructing the phase plot. The trajectory in d dimensional space is a set of k consecutive points and where is the starting time of observation.

Description of MethodsPhase space reconstructionFig. 1 ECG Signals Fig. 2 Attractors of an ECG form encryption person

Reconstructed time series

Calculating correlation integral

Correlation dimension - by Grassber & Procaccia (1983)Description of Methods

Lyapunov exponents are defined as the long time average exponential rates of divergence of nearby states. If a system has at least one positive Lyapunov exponent, than the system is chaotic. The larger the positive exponent, the more chaotic the system becomes.In general Lyapunov exponents are arranged such that , where and correspond to the most rapidly expanding and contracting principal axes, respectively. Therefore, may be regarded as an estimator of the dominant chaotic behavior of a system. Description of MethodsLyapunov Exponent

Description of MethodsLyapunov ExponentThe largest Lyapunov exponent is treated as a measure of the ECG signal using the wolf algorithm. The process of determination is listed as follows: 1. Compute the separation do of nearby two points in the reconstructed phase space orbit.2. Come next both points as they move a short distance along the orbit. Calculate the new separation d1.3. If becomes do too large, keep one of the points and choose an appropriate replacement for other point.4. Repeat Steps 1-3 after propagations, the largest Lyapunov exponent should be calculated viawhere .

Chaotic FunctionsChaotic Logisitic Map.The logistic map is a polynomial mapping of second order which chaotic behavior for different parameters proposed by the biologist Robert May.where n=0,1,2,, Fig 5. Bifurcation diagram for the logistic map Fig 6. Lyapunov exponents of the logistic map A is a (positive) bifurcation parameter.

Property of logistic map with different bifurcation parameter with L0=0.1(a) A=2.7(b) A=3.1(c) A=3.8

Chaotic Henon Map.The Henon map is a 2-D iterated map with chaotic solutions proposed by Mchel Henon (1976).where a and b are (positive) bifurcation parametersFig 3. Attractors for the Henon map with a=1.4;b=0.3Fig 4. Bifurcation diagram for the Henon map, b=0.3Chaotic Functions

system parameters a=10; b=28; c=8/3Initial values x0=-7.69; y0=-15.61; z0=90.39The Lorenz system by Edward Lorenz(1963)Chaotic Functions

They are aperiodic.

They exhibit sensitive dependence on initial conditions and unpredictable in the long term.

They are governed by one or more control parameters, a small change in which can cause the chaos to appear or disappear.

Their governing equations are nonlinear.Characteristics of Chaotic Systems

ECG-Based Biometric Recognition

Normal 12-Lead ECGStandard-leadsAugmented-leadsChest-leads

2

y

Lead I

Lead II

Lead III

L

R

F

VIII = F - L

VII = F - R

VI = L - R

LA

RA

LF

RF

2

y

L

R

F

L

R

F

aVR

aVF

L

R

F

aVL

1

V1

V2

V3

V4

V5

V6

ECG Waveform from electrical activities of heartP waveAtriumDepolarization QRS waveVentricular Depolarization

T wave Repolarization

A novel handheld device ET-600

Bio-potentialsensor

ESD protection circuits

First active sensor electorde

Second active sensor electorde

Bio-signalmeasurement

Buffer/balancedcircuit

ananlogfilter/amplifier unit

negative feedback difference common mode signal

associative processing unit

signal processingunit

external input device

display device

Lead I(two contact points)Collected from lab.19NoChaos Theory+BPNNOur research[28-30]Standard Lead ICollected from lab.26YesFSESilva et al.[37]standard Lead ICollected from lab.19NoCross Parsing+MDLCoutinho et al.[39]standard Lead ICollected from lab.15NoLPC+WPDLoong et al.[36]standard Lead IMIT-BIH35NoWavelet DistanceChiu et al.[21]Standard 12-leadsMIT-BIH/PTB14NoWavelet+LDAFatemian et al.[38]standard Lead ICollected from lab.10NoHigh-orderLegendre PolynomialsKhalil et al.[35]standard Lead ICollected from lab.50No Wavelet Distance Chan et al.[24]Standard 12-leadsMIT-BIH/PTB13NoAC/DCT+KNNWang et al.[25]Standard Lead IIMIT-BIH/PTB56NoLDA+PCAAgrafioti et al[26].Standard 12-leadsCollected from lab.29YesLDAIsrael et al.[22]standard Lead IMIT-BIH20YesTempl. Matching+DBNNShen et al.[33]Standard Leads (I,II,III)MIT-BIH20YesPCABiel et al.[20]Electrode OrientationData SourceRecognition Rate*No. of Tested SubjectsFiducial DetectionMethod[*]value claimed in the paperComparison of related works with the proposed methodECG-Based Biometric Recognition

Classification of ECG-Based Biometric Techniques Direct Time-Domain Feature Extraction Intervals (PQ, PR, QT intervals ) Durations (P, QRS, T durations) Amplitudes (P, QRS, T amplitudes) Slope (ST slope) Segment (ST segment)

Frequency-Domain Feature Extraction Wavelet Decomposition Fourier Transform Discrete Cosine Transform

Chaos Feature Extraction Lyapunov Exponents Correlation Dimension

Age, height and weight of 19 subjects joining the experiment

Sub. ASub. BSub. CSub. DSub. ESub. FSub. GSub. HSub. ISexFemaleMaleMaleMaleMaleMaleFemaleFemaleMaleAge(Yr)252725243124221753Height(cm)153172175173170166152158173Wight (kg)507068746560404768

Sub. JSub. KSub. LSub. MSub. NSub. OSub. PSub. QSub. RSub. SSexMaleMaleFemaleMaleFemaleMaleFemaleFemaleFemaleMaleAge(Yr)24241936324023272733Height(cm)175180156175166173155160155177Wight (kg)71754669537249504577

Distribution of the 19 subjects characteristics and their Centroids

Distribution of the 19 subjects characteristics and their Centroids

Distribution of the 19 subjects characteristics and their Centroids

Distribution of the 19 subjects characteristics and their Centroids

Synchronization of two identical Lorenz systems

where di0(i=1,2,3) are coupling coefficients ei(t) are error states, ei(t) 0 (t , i = x, y, z)

Linear Coupled Feedback Synchronization Control

Chaotic phase trajectories for two Lorenz system

Simulations of Synchronization Control of Lorenz System with ECG Signal

Component list of Lorenz-Based chaotic masking communication circuitsHardware Implementation of the Lorenz-based Oscillator

Element numberDescriptionValueToleranceU1~U5Op Amp (LF412)R1,R4,R8~R18,R21,R231/4W Resistor10 K0.05%R2,R191/4W Resistor374 K0.05%R3,R201/4W Resistor35.7 K0.05%R5,R221/4W Resistor1 M0.05%R6,R7,R23,R241/4W Resistor100 K0.05%C1~C6Capacitor0.1F0.1%M1~M4Analog multiplier

Lorenz-Based chaotic masking communication circuit

Hardware Implementation of the Lorenz-based OscillatorPhase portraits of Lorenz oscillator(a)x-y plane (b)x-z plane (c)y-z plane

Synchronization of Lorenz-Based CircuitsThe synchronization of the driver signal x1 and response signal x2(a) numerical plot (b) experimentally obtained

Synchronization of Lorenz-Based Circuits with ECG SignalChannel 1: private key ECG_signal, Channel 2: transmitted chaotic signal ECG_masking, Channel 3: recovered private key ECG_signal in the response system(a) numerical plot (b) experimentally obtained

The testing scene of self-developed ECG acquisition system with Lorenz-based circuits

System Design and Secure Information Transmission

Concept of CryptographyClassical cryptography - Caesar displacement

PlaintextTHE QUICK BROWN FOX JUMPS OVER THE LAZY DOGCiphertextWKH TXLFN EURZQ IRA MXPSV RYHU WKH ODCB GRJ

Concept of CryptographyModern cryptography

When K1=K2, the system is called symmetric encryption system.

When K1 K2, the system is called asymmetric encryption system, where K1 is called public key and K2 is called secret key.

Information encryption and decryption schemes

Structure of the secure information transmission

Recipient

Public Channel

Sender

Experimental Results and Discussion

Experimental ParametersTABLE IIDifferent Kinds of ImagesTABLE IComputed of ECG Signals for Different Persons TABLE IIIParameters of chaotic function for Encryption and DecryptionTABLE IVParameters of chaotic function for Decryption

FilenameSizeColor typecameraman.tif256 2568 bits grayscalekids.tif318 4008 bits indexedpeppers.png512 38424 bits RGB

Person 1Person 2Person 30.02390.02430.0241

ItemsValueDescriptionn1500number of iterations0.0239initial value formed byof the person 1a1.4system parameter of Henon mapb0.3system parameter of Henon mapA4system parameter of logistic map

ItemsValueDescriptionn1500number of iterations0.0243initial value formed byof the person 2a1.4system parameter of Henon mapb0.3system parameter of Henon mapA4system parameter of logistic map

Simulation Results -8 bits grayscale Fig 5. Encryption and Decryption for case 1 (a) original image. (b) histograms of original image. (c) encrypted image. (d) histograms of encrypted image. (e) wrong decrypted image. (f) correct decrypted image.

Simulation Results - 8 bits indexedFig 6. Encryption and Decryption for case 2 (a) original image. (b) histograms of original image. (c) encrypted image. (d) histograms of encrypted image. (e) wrong decrypted image. (f) correct decrypted image.

Simulation Results - 24 bits GRBFig 7. Encryption and Decryption for case 3 (a) original image. (b)(c)(d) The histograms of red, green and blue channels of original image respectively. (e) The encrypted image. (f)(g)(h) The histograms of red, green and blue channels of encrypted image respectively. (i) wrong decrypted image. (j) correct decrypted image.

Simulation Results - Document Blended with Figures and Text Fig 9. Ciphertext of the original document Fig 8. original document

Simulation Results - Document Blended with Figures and Text Fig 11. Resulting correct decrypted plaintext Fig 10. Wrong decrypted plaintext

Key Space AnalysisComparison of features of DES, Triple-DES, IDEA, PCEA

EDSTriple-DESIDEAPCEAKey space (bits)56112 or 168128Depend on chaotic maps properties, can be ideally infinityNo. of rounds164881No. of sub-keys1648545 (bifurcation parameters: (A,a,b), initial value ( ), no. of iterations (n))Key generationShift permuteShift permuteShiftingIteration of the chaotic mapsBlock size (bits)646464-MathematicalOperationXOR, fixed S-boxesXOR, fixed S-boxesXOR, Addition, MultiplicationAddition,Subtraction,MultiplicationExistence of attack Broken: Brute Force, 1998No known attackNo known attackNo known attack

Conclusions This study has presented a theoretical and experimental study on chaos synchronization and masking of data communication using electronic devices described by the Lorenz equations, and showed that the private key created by ECG signals can be recovered from a chaotic carrier using a response system whose chaotic dynamics is synchronized with a driver system.

We investigate the use of ECG signal features from nonlinear dynamic modeling for information encryption, and present a personalized encryption scheme based on the individual-specific features of ECG as a personal key.

Experimental results have proved its feasibility and effectiveness. Moreover, the encryption time shows its applicability in real-time applications for image encryption and data transmission. The proposed system can thus be used for personalized secure data storage and transmission.

Future Work The chaotic features of ECG that the characteristic distributions appear the proposed features would be appropriate for biometric identification. In the current stage of this research, the recognition process was conducted off-line and the effectiveness of the proposed methods was tested on the normal rest subjects. Investigation of the on-line operation to demonstrate possibility of the proposed verification systems in practical applications such as the tested subjects are under stressed or after exercise should be further studied.

On the application side, our proposed biometric identification can be applied to the access control system, such as electromagnetic induction cards, locks, etc.

List of Publication[J1] C. K. Chen, C. L. Lin, C. T. Chiang, and S. L. Lin, Information encryption using ECG signals with chaotic functions, Information Sciences, vol. 193, pp. 125-140, 2012. (SCI, Impact Factor:3.291, Rank:6/116) (NSC-99- 2221-E- 005-066).

[J2] C. K. Chen, C. L. Lin, S. L. Lin, Y. M. Chiu, and C. T. Chiang, A Chaotic Theoretical Approach to ECG-Based Identity Recognition , IEEE Computational Intelligence Magazine , 2013, (SCI) (NSC-99-2221-E-005-066), revised.

[J3] C. K. Chen, C. L. Lin, C. T. Chiang, and S. L. Lin, Personalized Information Encryption Using ECG Signals with Chaotic Functions and Secure Transmissions via Synchronized Circuits, IEEE Transactions on System, Man ,and Cybernetics, Part B, 2013, (SCI) (NSC-99-2221-E-005-066), revised.

[C1] C. K. Chen, C. L. Lin, and Y. M. Chiu, Data Encryption Using ECG Signals with Chaotic Henon Map, in Proc. of International Conference on Information Science and Applications, pp. 1-5, Seoul, Korea, 2010.

[C2] C. K. Chen and C. L. Lin, Text Encryption Using ECG signals with Chaotic Logistic Map, in Proc. of IEEE International Conference on Industrial Technology, pp. 1741-1746, Taichung, Taiwan, 2010.

[C3] C. K. Chen, C. L. Lin, and Yen-Ming Chiu, Individual Identification Based on Chaotic Electrocardiogram Signals, in Proc. of IEEE International Conference on Industrial Technology, pp. 1765-1770, Beijing, China, 2011.

[C4]Y. H. Hsu, C. L. Lin, C. K. Chen, C. T. Chiang, and W. T. Yang, Health Care Platform Based on Acquisition of ECG for HRV analysis, in Proc. of International Conference on Industrial Informatics, Beijing, China, 2012.

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