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

2. 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

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

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

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

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

7. 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

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

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

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

11. 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

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

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

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

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

16. 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

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 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

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

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 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

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 Set (B) (Favorite movies) Candidate point
identification
Error-correcting
codes
(Reed-Solomon codes)
Vault (VA)
Polynomial (p)
reconstruction
Secret data (S)
(Tel #)

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 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

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

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. (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)

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
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

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
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

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