CRYPTO KEY GENERATION USING SLICING WINDOW ALGORITHM

M.S. Altarawneh, L.C. Khor, W.L. Woo, and S.S. DlaySchool of Electrical, Electronic and Computer Engineering

University of NewcastleNewcastle upon Tyne, NE1 7RU

UNITED KINGDOMEmail: {mokhled.al-tarawneh, l.c.khor, w.l.woos. s.dlay} @ncl.ac.uk

Abstract• Background

Fingerprint minutiae points are used for generating cryptographic key. Slicing window partitioning formation on base Euclidean distance between detected core and minutiae points. Generated vector used to derive an encryption key.

• Challenges Security at your fingertips. Bio-Crypto Key Generation. However, approach has to be validated based on consistency to avoid false positives: relatively little work done in the field of cryptographic key- generation.

•ContributionsA novel approach to generate encryption key from fingerprint sample is introduced. Experimental analysis show encouraging prospects.

Outline Introduction

Prior related work Proposed Approach

Mathematical representation of RP detecting algorithm Slicing window construction Key generation

Experimental Evaluation Conclusion

Introduction•Using biometric data as a basis for cryptographic keys is problematic:

biometric measurement is not perfectly reproducible.

cryptography relies on a stable and unique key to encrypt and decrypt texts.

•Incorporation address approaches of biometric - cryptography :

Key release algorithms.

key generation algorithms.

Proposed Approach

Slicing Window Construction

ReferencePoint Detection

Minutiae Extraction

Key Generation

SW<=MAS

YES

NO

Proposed Approach

xf xf

2yx iffz

• Orientation tensor image field computation: ReferencePoint Detection

where and denote the derivatives of the image in x and y direction respectively

xf yf

• Complex filter computation:

2

22

2exp.,

yx

iyxyxc m

Where represents filter order and is standard deviation of modulated filter which is in this case modulated by Gaussian envelope

m

• Mathematical representation of reference point (RP) detecting algorithm.

• applying Conditional/ Crossing Number (CN) concept, CN extracts the ridge points from the skeleton image.

ReferencePoint Detection

Minutiae Extraction

iP PWhere is the pixel belonging to the neighbourhood of

NxM• Extracted minutiae points contain:

ix iy

it idWhere is the x-coordinate position, is the y-coordinate position,

is the type and distance of a particular minutiae.

19,||5.08

11 pPPPCN

iii

nidtyx iiiii ...1|,,,|

Slicing Window Construction

ReferencePoint Detection

Minutiae Extraction

SW<=MAS

YES

• RP and minutiae points distance determined by Euclidean distance form:

22mrmr yyxxD

rr yx , mm yx ,Where is the reference point coordination and is the minutiae point coordination.

x y t d

9 124 6 85.094

14 182 2 96.519

24 115 2 71.197

24 182 2 88.408

28 144 6 67.912

30 152 2 68.352

34 24 6 120.07

For i=1: T; // T is template sizeWindow size=64x64Do minutiae counting entire window;Vector generating; // number of minutiae by window sizeNext windows; // 128… 256, till end of template sizeEndEnd

• Minutiae point coordination's and slicing window algorithm

Slicing Window Construction

ReferencePoint Detection

Minutiae Extraction

SW<=MAS

YES

36, 182

39, 154

34, 24

RP

24, 1159, 124

14, 182 24, 182

30, 152

34, 143

28, 144

0

20

40

60

80

100

120

140

160

180

200

0 20 40 60 80 100

Window construction on base of template information.

Slicing Window Construction

ReferencePoint Detection

Minutiae Extraction

Key Generation

SW<=MAS

YES

NO

• vector generation:

V=slicing window size * minutiae points’ quantity

V={Header locker key and Encryption provider key }

Header locker key (HLK) will be produced by V1, V3 concatenating.

Encryption provider key (EPK) by V2, V4 concatenating.

1110

3

1101

1

V

V

1110

4

1101

2

V

V

11101101 11101101

Example of generated Vector

HLK EPK

Experimental Analyses• Test environment is:

400 fingerprints images database (TIFF, format, 300x300 sizes, and 500 dpi resolutions).

Tests show that generated key length is depend on: extracted minutiae points and their positions in slicing windows.

Test shows 100% uniqueness of generated keys.

• Resistance brute force attacks of our approach is increased by:

Entropy of applicable system feed by HLK and EPK.

Two secure circles, cipher header closing and plain text encoding.

Conclusion• Our approach takes advantage of fingerprint template extracted information and standard encryption algorithms to provide a novel way of generating cipher keys without having to remember complicated sequences which might be lost, stolen, or even guessed.

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