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**Securing Fingerprint Template - Fuzzy Vault with Helper Data**

Presenters: Yeh Po-Yin Yang Yi-Lun

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**Outline Review Previous Work Proposed System Experimental Results**

Introduction – Fuzzy vault Proposed System Encoding Decoding Experimental Results Genuine Accept Rate (GAR) False Accept Rate (FAR) Conclusions

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**Registration Alice Template Alice**

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

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**Template is stored Alice**

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

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**Authentication Alice Alice**

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

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** Authentication It’s Alice! ? Alice**

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

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**Templates represent intrinsic information about you**

Alice Alice Theft of a template is theft of identity [3] Ari Juels RSA Labortories 10th CAR Information Security Workshop 8 May, 2002

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**Server-side matching Alice Client Server**

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

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**Server-side matching Alice Alice Client Server **

“access granted” Server Alice [3] Ari Juels RSA Labortories 10th CAR Information Security Workshop 8 May, 2002

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**Client-side matching Alice “Hi, Alice!” “It’s Alice!” Server **

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

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**Client-side matching Alice “Hi, Alice!” “It’s Alice!” “It’s Alice!”**

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

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**On-device matching Alice Alice**

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

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**On-device matching Alice Alice Alice **

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

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**UNIX protection of passwords**

h h(“password”) “password” “password” “password” [3] Ari Juels RSA Labortories 10th CAR Information Security Workshop 8 May, 2002

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**Template protection? h( ) h Alice Alice Alice**

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

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**Fingerprint is variable**

Alice 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

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**Introduction – Fuzzy vault**

Juels and Sudan (2002) The movie lover problem - encrypt Alice 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

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**Introduction – Fuzzy vault**

The movie lover problem – decrypt Bob 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 # )

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Encoding Alice partitions her secret value (S) into shares s1, s2,..., sk Construct a polynomial (p) so that p(xj) = sk-1xk-1 + sk-2xk 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

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**Flow chart - Encode + Set (A) (Favorite movies) Secret data (S)**

(Tel #) Polynomial (p) construction Polynomial Projection Chaff Point Generation (C) + Vault (VA)

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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

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**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

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**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

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**Flow chart - Decode Set (B) (Favorite movies) Candidate point**

identification Error-correcting codes (Reed-Solomon codes) Vault (VA) Polynomial (p) reconstruction Secret data (S) (Tel #)

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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

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**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

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**Fuzzy Fingerprint Vault**

Replace 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

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**Encode Error-correction scheme 128bit secret + 16bit CRC = 144bit (S)**

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 + s7x s1x + s0 Quantify minutiae data (A) Apply fuzzy vault scheme → get VA Construct helper data

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**(Quantified minutiae)**

Set (A) (Quantified minutiae) Polynomial (p) construction Polynomial Projection Secret data (S) CRC encoding Chaff Point Generation (C) + Vault (VA)

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**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 / 11 = Remainder is not zero : error!

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**(Quantified minutiae)**

Set (B) (Quantified minutiae) Vault (VA) Candidate point identification Error-correcting codes (Reed-Solomon codes) Polynomial (p) reconstruction CRC decoding Secret data (S)

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**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

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**Constructing Helper Data**

Find 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

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**Helper Data Filtering outliers points with too low curvature**

points with too high curvature

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[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

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**ICP base Alignment Iterative Closest Point**

Estimate the initial transformation Find the center of mass 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

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[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

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[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

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**Experiments DB2 database of FVC 2002 study**

8 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

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**Results Two impression per finger are used**

1 for locking, 1for unlocking GAR = 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)

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**Conclusion Secured 128-bit AES keys feasibly**

An automatic alignment scheme based on helper data derived from the orientation field of fingerprints The helper data does not leak any information about the minutiae-based fingerprint template User is expected to be cooperative Reduce false rejects

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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, Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'06), 2006 [3] Ari Juels RSA Labortories 10th CAR Information Security Workshop 8 May, 2002

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