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Securing Fingerprint Template - Fuzzy Vault with Helper Data Presenters: Yeh Po-Yin Yang Yi-Lun.

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Presentation on theme: "Securing Fingerprint Template - Fuzzy Vault with Helper Data Presenters: Yeh Po-Yin Yang Yi-Lun."— Presentation transcript:

1 Securing Fingerprint Template - Fuzzy Vault with Helper Data Presenters: Yeh Po-Yin Yang Yi-Lun

2 Outline Review Previous Work Introduction – Fuzzy vault Proposed System Encoding Decoding Experimental Results Genuine Accept Rate (GAR) False Accept Rate (FAR) Conclusions

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

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

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

6 Authentication  ? It’s Alice! [3] Ari Juels RSA Labortories 10th CAR Information Security Workshop 8 May, 2002

7 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

8 Server-side matching Server Client [3] Ari Juels RSA Labortories 10th CAR Information Security Workshop 8 May, 2002

9 Server-side matching Server Client  “access granted” [3] Ari Juels RSA Labortories 10th CAR Information Security Workshop 8 May, 2002

10 Client-side matching Server  “It’s Alice!” “Hi, Alice!” [3] Ari Juels RSA Labortories 10th CAR Information Security Workshop 8 May, 2002

11 Client-side matching Server “It’s Alice!” “Hi, Alice!” [3] Ari Juels RSA Labortories 10th CAR Information Security Workshop 8 May, 2002

12 On-device matching SecurID [3] Ari Juels RSA Labortories 10th CAR Information Security Workshop 8 May, 2002

13 On-device matching  SecurID [3] Ari Juels RSA Labortories 10th CAR Information Security Workshop 8 May, 2002

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

15 Template protection? h( ) [3] Ari Juels RSA Labortories 10th CAR Information Security Workshop 8 May, 2002

16 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

17 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

18 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 # )

19 Encoding Alice partitions her secret value (S) into shares s 1, s 2,..., s k Construct a polynomial (p) so that p(x j ) = s k-1 x k-1 + s k-2 x k s 1 x + s 0 For every element in set A, find ( a i, p(a i ) ) Projecting elements of A onto p Create chaff points that do not lie on p Call this collection of points R

20 Flow chart - Encode Secret data (S) (Tel #) Polynomial (p) construction Polynomial Projection Chaff Point Generation (C) Set (A) (Favorite movies) + Vault (V A )

21 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

22 Decoding Project R onto b i (elements in set B) If there exists a pair (bi, y) in R for any y then (x i, y i ) = (b i, y), else (x i, y i ) = null Call this collection of points Q Perform the Reed-Solomon decoding algorithm and reconstruct a unique polynomial p

23 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

24 Flow chart - Decode Vault (V A ) Secret data (S) (Tel #) Set (B) (Favorite movies) Error-correcting codes (Reed-Solomon codes) Polynomial (p) reconstruction Candidate point identification

25 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

26 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

27 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

28 Encode Error-correction scheme Cyclic Redundancy Check (CRC) 16bit CRC: g CRC (a) = a 16 + a 15 + a bit secret + 16bit CRC = 144bit (S) divided into 9 non-overlapping 16-bit segments ( 144/16 = 9) p(x) = s 8 x 8 + s 7 x s 1 x + s 0 Quantify minutiae data (A) Apply fuzzy vault scheme → get V A Construct helper data

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

30 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!

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

32 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

33 Constructing Helper Data Find the orientation field that shows the dominant orientation in each block 8*8 s j = s j-1 + d j * l j * o S j-1 j = the index of points on the curve d j = the flow direction between s j and s j-1 { -1, 1 } l j = the length of line segment between these two points o S j-1 = the orientation value at location s j-1

34 Helper Data Filtering outliers points with too low curvature points with too high curvature

35 [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

36 ICP base Alignment Iterative Closest Point 1.Estimate the initial transformation Find 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

37 [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

38 [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

39 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

40 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)

41 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

42 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


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