Securing Fingerprint Template - Fuzzy Vault with Helper Data

Presenters:

Yeh Po-Yin

Yang Yi-Lun

Outline

ReviewPrevious Work

Introduction – Fuzzy vault

Proposed SystemEncodingDecoding

Experimental ResultsGenuine Accept Rate (GAR)False Accept Rate (FAR)

Conclusions

Registration

Template

Alice

[3] Ari Juels RSA Labortories 10th CAR Information Security Workshop 8 May, 2002

Template is stored

[3] Ari Juels RSA Labortories 10th CAR Information Security Workshop 8 May, 2002

Authentication

[3] Ari Juels RSA Labortories 10th CAR Information Security Workshop 8 May, 2002

Authentication

?

It’s Alice!

[3] Ari Juels RSA Labortories 10th CAR Information Security Workshop 8 May, 2002

Templates represent intrinsic information about you

Alice

Theft of a template is theft of identity

[3] Ari Juels RSA Labortories 10th CAR Information Security Workshop 8 May, 2002

Server-side matching

Server

Client

[3] Ari Juels RSA Labortories 10th CAR Information Security Workshop 8 May, 2002

Server-side matching

Server

Client

“access

granted”

[3] Ari Juels RSA Labortories 10th CAR Information Security Workshop 8 May, 2002

Client-side matching

Server

“It’s Alice!”“Hi, Alice!”

[3] Ari Juels RSA Labortories 10th CAR Information Security Workshop 8 May, 2002

Client-side matching

Server

“It’s Alice!”“It’s Alice!”“Hi, Alice!”

[3] Ari Juels RSA Labortories 10th CAR Information Security Workshop 8 May, 2002

On-device matching

SecurID

[3] Ari Juels RSA Labortories 10th CAR Information Security Workshop 8 May, 2002

On-device matching

SecurID

[3] Ari Juels RSA Labortories 10th CAR Information Security Workshop 8 May, 2002

“password”

UNIX protection of passwords

“password” h(“password”)

“password”

[3] Ari Juels RSA Labortories 10th CAR Information Security Workshop 8 May, 2002

Template protection?

h( )

[3] Ari Juels RSA Labortories 10th CAR Information Security Workshop 8 May, 2002

Fingerprint is variable

Differing angles of presentation

Differing amounts of pressure

Chapped skin

Don’t have exact key!So hashing won’t work...

[3] Ari Juels RSA Labortories 10th CAR Information Security Workshop 8 May, 2002

Introduction – Fuzzy vault

Juels and Sudan (2002)

The movie lover problem - encryptAlice has a set of favorite movies (A)

Alice wants to give her Tel # to someone with the same interests

Alice does not want the others to know about her interests

Alice encrypt her Tel # under A

Introduction – Fuzzy vault

The movie lover problem – decryptBob has a set of favorite movies (B)

If B is identical to A, then Bob gets Alice’s Tel #

If B is different than A, then Bob gets nothing

If B is similar to A, then Bob might get Alice’s Tel # ( depend on the algorithm Alice used to encrypt he Tel # )

EncodingAlice partitions her secret value (S) into shares s1, s2,..., sk

Construct a polynomial (p) so that

p(xj) = sk-1xk-1 + sk-2xk-2 + ... + s1x + s0

For every element in set A,

find ( ai, p(ai) )

Projecting elements of A onto p

Create chaff points that do not lie on p

Call this collection of points R

Flow chart - Encode

Secret data (S)(Tel #)

Polynomial (p)construction

PolynomialProjection

Chaff PointGeneration (C)

Set (A)(Favorite movies)

+

Vault (VA)

Algorithm Lock

[1] A. Juels and M. Sudan. “A fuzzy vault scheme.” In A. Lapidoth and E. Teletar, editors, Proc. IEEE Int. Symp. Information Theory, 2002

Decoding

Project R onto bi (elements in set B)

If there exists a pair (bi, y) in R for any y

then (xi, yi) = (bi, y), else (xi, yi) = null

Call this collection of points Q

Perform the Reed-Solomon decoding algorithm and reconstruct a unique polynomial p

Reed-Solomon decoding

The classical algorithm of

Peterson-Berlekamp-Massey decodes successfully if at least (k+t)/2 points in Q share a common polynomial

t = the # of points in set A

k = a polynomial of degree less than k

Flow chart - Decode

Vault (VA)

Secret data (S)(Tel #)

Set (B)(Favorite movies)

Error-correctingcodes

(Reed-Solomon codes)

Polynomial (p)reconstruction

Candidate pointidentification

Algorithm Unlock

[1] A. Juels and M. Sudan. “A fuzzy vault scheme.” In A. Lapidoth and E. Teletar, editors, Proc. IEEE Int. Symp. Information Theory, 2002

Security

Depends on the # of chaff points r-t in the target set R

r = the total # of points

t = the # of points in set A

Attacker cannot distinguish between the correct polynomial p and all of the spurious ones

Security proportional to the # of spurious polynomials

Fuzzy Fingerprint VaultReplace favorite movies with Fingerprint minutiae data

Different from favorite movies, minutiae data needs to be aligned

without any information leak

Quantification is applied to account for slight variations in minutiae data

example:block size = 11x11

[1,11] → 6

EncodeError-correction scheme

Cyclic Redundancy Check (CRC)

16bit CRC: gCRC(a) = a16 + a15 + a2 +1

128bit secret + 16bit CRC = 144bit (S)divided into 9 non-overlapping 16-bit segments ( 144/16 = 9)

p(x) = s8x8 + s7x7 + ... + s1x + s0

Quantify minutiae data (A)

Apply fuzzy vault scheme → get VA

Construct helper data

Secret data (S)Polynomial (p)construction

PolynomialProjection

Chaff PointGeneration (C)

Set (A)(Quantified minutiae)

+

Vault (VA)

00110100

CRC encoding

0011010011

Decode

Quantify minutiae data (B)

Apply fuzzy vault scheme → get p

Decrypt secret from p (144bit code)

Apply CRC coding to check whether there are errors in this secret

Divide the secret with CRC code

0011010011 / 11 = 00110100 ..... 0Remainder is not zero : error!

Secret data (S)

Set (B)(Quantified minutiae)

Vault (VA)

00110100

CRC decoding 0011010011

Candidate pointidentification

Error-correctingcodes

(Reed-Solomon codes)

Polynomial (p)reconstruction

Helper data

Orientation Field Flow Curves (OFFC)Sets of piecewise linear segments the represent the underlying flow of fingerprint ridges

Robust to noise minutiae

islands

smudges

cuts

Constructing Helper DataFind the orientation field that shows the dominant orientation in each block

8*8

sj = sj-1 + dj * lj * oSj-1

j = the index of points on the curve

dj = the flow direction between sj and sj-1

{ -1, 1 }

lj = the length of line segment between these two points

oSj-1 = the orientation value at location sj-1

Helper Data Filtering

outliers

points with too low curvature

points with too high curvature

[2]Umut Uludag, Anil Jain, "Securing Fingerprint Template: Fuzzy Vault with Helper Data," cvprw, p. 163, 2006 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'06), 2006

ICP base AlignmentIterative Closest Point

1. Estimate the initial transformationFind the center of mass

2. Iterate until convergence

C: curvature, r: row, c: column

higher αvalues emphasize the effect of curvature

(100, 150, 400)

[2]Umut Uludag, Anil Jain, "Securing Fingerprint Template: Fuzzy Vault with Helper Data," cvprw, p. 163, 2006 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'06), 2006

[2]Umut Uludag, Anil Jain, "Securing Fingerprint Template: Fuzzy Vault with Helper Data," cvprw, p. 163, 2006 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'06), 2006

[2]Umut Uludag, Anil Jain, "Securing Fingerprint Template: Fuzzy Vault with Helper Data," cvprw, p. 163, 2006 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'06), 2006

Experiments

DB2 database of FVC 2002 study8 impressions for each of the 100 distinct fingers

Image size: 560*296

Resolution: 569dpi

Block size: 11*11

24 genuine minutiae points dispersed among 200 chaff points are used

ResultsTwo impression per finger are used

1 for locking, 1for unlockingGAR = 72.6% at FAR = 0%

has less than 24 minutiae (16)

Unlocking with two impression per finger

GAR = 84.5% at FAR = 0%errors in helper data (7)poor quality image (4)common minutiae between locking and unlocking prints less than the required number (2)

Conclusion

Secured 128-bit AES keys feasiblyAn automatic alignment scheme based on helper data derived from the orientation field of fingerprintsThe helper data does not leak any information about the minutiae-based fingerprint template User is expected to be cooperative

Reduce false rejects

References[1] A. Juels and M. Sudan. “A fuzzy vault scheme.” In A. Lapidoth and E. Teletar, editors, Proc. IEEE Int. Symp. Information Theory, 2002[2]Umut Uludag, Anil Jain, "Securing Fingerprint Template: Fuzzy Vault with Helper Data," cvprw, p. 163, 2006 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'06), 2006[3] Ari Juels RSA Labortories 10th CAR Information Security Workshop 8 May, 2002