1. Secure and Privacy-Preserving User Authentication Using Biometrics
Monday, 18/12/2017
Nikolaos Theodorakis

2. Research Motivation
Fingerprint biometric systems still do not provide efficient security/accuracy
Compatibility and privacy issues have not been addressed correctly
Aim to improve performance of minutiae-based protected systems by investigating and experimenting on trade-offs

3. Thesis Outline Theory & Background Fuzzy Vault Implementation Results
Future Research Plan

4. Biometric System Modules
Sensor
Feature Extraction
Matching
Database 4

5. Enrollment & Matching Enrollment Matching Template stored in database
Verification (one-to-one)
Identification (one-to-many) 5

6. Metrics Two types of errors: Comparisons between false/true attempts:
False Match (false positive)
False Non-Match (false negative)
Comparisons between false/true attempts:
FAR (False Acceptance Rate)
FRR (False Rejection Rate)
GAR (Genuine Acceptance Rate)
EER (Equal Error Rate)
ROC curves / DET curves
FTCR (Failure to Capture Rate)
FTQR (Failure to Quantize Rate) 6

7. Fingerprint Minutia m = { x, y, θ, t } x,y : coordinates
θ : orientation angle
t : type 7

8. Requirements Security Requirements Privacy Requirements
Confidentiality
Integrity
Availability
Renewability / Revocability
Privacy Requirements
Identity Privacy
Irreversibility
Unlinkability

9. Fuzzy Vault (1) Protects a secret k under a set A
Encodes k as coefficients of a polynomial p
Projects A to p over F(2m) as genuine points
Merges and shuffles {x, p(x)} along with random points as noise (chaff)
Unlocks vault when a set B overlaps substantially with A, by locating genuine points (polynomial reconstruction problem) 9

10. Fuzzy Vault (2) 10

11. Fuzzy Vault Security Security Analysis Attacks
Full brute-force attack: 𝑛 𝑑+1 (1)
Combinations unlocking vault: 𝑔 𝑑+1 (2)
Average time: (1) (2)
Attacks
Record Multiplicity
Key Inversion
Blended Substitution 11

12. Alignment Problem Statement
How to align two templates if one is encrypted?
Additional information is needed (helper data)
Helper data reveals information about the user
Minutiae based helper data could unlock the vault or reveal part of the fingerprint
Non-minutiae based helper data cannot be used in already implemented systems without access to the fingerprint image

13. Alignment Method (1) Enrollment: Matching:
Triangle structures of minutiae points (3 minutiae for each triangle) as helper data at enrollment
Each minutia: m = {x,y,θ,quality,type}
Each triangle: m1x,m1y,m2x,m2y,m3x,m3y, r1, r2, r3, m1θ, m2 θ, m3 θ, φ1, φ2, φ3, m1type, m2type, m3type
Matching:
Locating almost identical triangles in the query template according to dr, dθ, types
Outputs possible transformation sets: dx, dy, referencePointx, referencePointy , dθ
Translate and Rotate query template accordingly

14. Alignment Method (2)

15. Alignment Method (3)

16. Alignment Evaluation
Investigate proximity between minutiae points after a transformation
For every helper data available: minimum distance of every TE point to TQ point as minDists
minPercentile = k-th percentile of minDists for every helper data as best transformation
For various θ values: θPercentage = 𝑛𝑜. 𝑜𝑓 𝑝𝑜𝑖𝑛𝑡𝑠 𝑤𝑖𝑡ℎ 𝑚𝑖𝑛𝐷𝑖𝑠𝑡<𝜃 𝑛𝑜. 𝑜𝑓 𝑡𝑜𝑡𝑎𝑙 𝑝𝑜𝑖𝑛𝑡𝑠 ∗100
Calculate EER, FAR, FRR, threshold

17. Fuzzy Vault Protection (1)
Polynomial Generation
Secret: S = [c1 c2 ... cd+1] ∈ F(2m)
length(S) = (d + 1) · m
p(x) = c1 · xd + c2 · xd− cd+1
Scaling and Quantization
m-bit representation (16-bit in our implementation)
x,y scaled to distribute uniformly across the image
x,y,θ quantized into 6,5,5 bit strings
m = x | y | θ as a 16-bit integer (0 – 65535)

18. Fuzzy Vault Protection (2)
Genuine Point Projection
p(mq) = c1 · mq + c2 · mq cd+1 · mq
GenuinePoints = [mq p(mq)] for all q ∈ genuine points
Chaff Points Creation
Random [chaffX chaffY] which do not belong to GenuinePoints
At least minDist distance to genuine points
Fuzzy Vault
Merge Genuine and Chaff Points and randomly shuffle rows

19. Fuzzy Vault Key Release
Partitioning
Fuzzy vault mq partitioned into x,y,θ (reversing quantization)
Distances and Subsets
Trying to locate a number of potentially genuine points based on distances between vault and input points
A larger number than d+1 is selected to try all possible combinations and increase chances of unlocking
Polynomial Reconstruction
A combination of d + 1 points reconstruct the polynomial by solving a system of equations
Secret is retrieved as the unknown variables of the system

20. Results (1) Database Evaluation Protocol
Minutiae database based on FVC2002DB1A
Good quality sample selected
Evaluation Protocol
Multi-Enrolment (1 query template against 1,2 and 3 enrolled)
Multi-Query (1 enrolled template against 1,2 and 3 query)
FAR and FRR computed 20

21. Alignment Evaluation (1)
trianglesNο.
percentile δr δθ
Found GAR
Found FMR 4 60 20
87%
59%
FRR
FAR
EER
Threshold
0.2169
0.2034
0.2101 21

22. Alignment Evaluation (2)
Best θ selection: θ = 20 22

23. Results (2) Degree chaffNo. FailureThreshold trianglesNo. subsetsNo. 7
200-minutiaeNo. 25 4 13
Multi-Query
Multi-Enrollment
Template No.
Gen.No
FRR
FAR 1 15
38.30
1.08
55.68 20
29.47
2.15
46.59
2.13 25
24.21
4.30
32.95
4.26 2
26.37
1.22
19.35
2.44
30.34
1.06
14.89
6.10
15.73
7.44 3
22.34
1.15
17.24
1.11
14.43
2.30
13.64
2.22
13.27
6.82
7.87 11
223

24. Results (3) 24

25. Conclusion Advantages Limitations Applicability Modular, decoupled
Ideal for minutiae-based legacy systems
Limitations
Performance degradation
Security and privacy risks due to the usage of minutiae
Little entropy
Applicability
User authentication, key protection
Mobile devices ( sensor & TEE )

26. Future Work Tweak and experiment with more parameters
Research on non-minutiae alignment methods
Further linkability analysis based on the helper data

27. Thank you for your attention !