1. Security Challenges of Biometric Systems
Liam M. Mayron, Ph.D.
Arizona State University
SoDA
January 29, 2015

2. Credit
Some of the reference material in this presentation is from the textbook “Introduction to Biometrics” by Jain, Ross, and Nandakumar
Some figures have been obtained from various online sources (as noted)

3. Biometrics?
Biometrics are physical and behavioral characteristics that uniquely identify humans
Typically used for authentication – associating individuals with their personal identities
Active area of research – combines image processing, security, information retrieval, physiology, cognitive science and other fields

4. Harry Potter
Source:

5. Harry Potter
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6. Harry Potter
Source:

7. What a person possesses
Identity
What a person knows
What a person possesses
Who a person is
Relying on what a person knows and what a person possesses is not enough!

8. Biometric functions “Are you who you say you are?”
Verification
Identification
“Are you who you say you are?”
“Are you someone who the system previously recognized?”

9. Examples of biometrics
Fingerprint
Palm print
Face
Iris
Retina
Ear
Voice
Signature
Gait
Hand
Vein
Odor
DNA
… AND MORE!

10. Fingerprint
Source:

11. Iris
Source:

12. Gait
Source:

13. System operation Enrollment phase Recognition phase
Sample biometric data
Extract features
Store extracted features, discard the raw data
Re-sample biometric data
Extract features
Compare against stored data
Determine user identity

14. System components Sensor Feature extractor Database Matcher
Green: enrollment
Purple: recognition
Sensor
Feature extractor
Database
Matcher

15. Feature extraction A lot of research interest!
Purpose is to generate a template, a compact representation of a biometric trait
Assess quality
Segment data
Enhance data

16. Matching Compare query data to a previously stored template
Decide if a individual is a genuine match or an imposter
Exact matches are… suspicious

17. Desired characteristics of biometrics
Uniqueness
Permanence
A biometric should be able to distinguish between two people
A biometric should not change (much) over time

18. Design cycle
Understand nature of application and performance requirements
Choose appropriate biometric traits
Collect sample biometric data
Design or train the feature extractor or matcher
Evaluation and feedback

19. Application considerations
Cooperative vs. non-cooperative users
Overt vs. covert deployment
Habituated vs. non-habituated users
Attended vs. unattended operation
Controlled vs. uncontrolled operation
Open vs. closed system

20. Biometric considerations
Universality
Uniqueness
Permanence
Measurability
Performance
Acceptability
Circumvention

21. Example: fingerprints
Source:

22. Fingerprints
Ridges under our fingers allow us to grasp objects and improve sensation
20-24 ridges per centimeter is typical
Ridge flow is a result of random stresses during fetal development*

23. Fingerprints
The template of a fingerprint is derived from its minutiae
Minutiae consist of:
Location: location in the image
Direction: direction along local ridge orientation
Type:
Ending
Bifurcation
A set of minutiae can potentially be used to derive the original ridge skeleton structure
Sets of minutiae are compared. If the difference is within allowable parameters both are considered to match

24. Security threats to biometric systems
Denial of Service (DoS)
Intrusion
Repudiation
Function creep

25. Infrastructure attacks
System attacks
Insider attacks
Infrastructure attacks
Biometric systems require human interaction – can be exploited
Collusion
Coercion
Negligence
Enrollment fraud
Exception abuse
Combination of hardware and software
Types
User interface
System modules
Interconnections
Template database

26. User interface attacks
Any attack initiated by presenting a biometric
Impersonation
Obfuscation
Spoofing
Spoof detection
Liveness detection
Measure physiological properties
Pulse
Blood pressure
Perspiration
Spectral properties of the skin
Electrical conductivity
Skin deformation
Identify voluntary/involuntary behavior
Challenge-response

27. Spoof detection
Source:

28. Interconnection attacks
Man-in-the-middle attack
Replay attack
Hill-climbing

29. Template database attacks
Leakage is a much more serious issue in biometric systems than in password-based systems

30. Leakage Ways information about a biometric user can be learned
Collusion or coercion: close proximity or cooperation
Covert acquisition: close proximity or cooperation
Brute force or hill-climbing: breach system security and intrusion
Template leakage: can be done remotely and anonymously
It is not possible to replace compromised biometric tokens
The irrevocable nature of biometrics is both a strength and a weakness

31. Password security
Can techniques that are used to store passwords be used to store biometric templates?
Encryption
Security depends on the secrecy of the decryption key
Encryption (done simply) is not enough to secure passwords
Password-based key generation
Password is never stored
Password is instead used to generate a cryptographic key
Cryptographic hash
A one-way hash is applied
Cannot retrieve the original password from the hash*

32. Requirements and challenges
Password security techniques cannot be directly applied to biometric templates
Fundamental difference between biometric password systems
Password systems require an exact match
Biometric systems require a “good” match
Biometric template protection requires
Cryptographic security: non-invertible templates
Performance: maintain matching ability
Revocability: generate multiple templates from the same data

33. Encryption Use encryption (AES, RSA) to secure template data
Not equivalent to password encryption
Passwords are the same
Biometrics vary at each reading
Can compare encrypted passwords directly; cannot directly-compare encrypted biometrics
Disadvantage: original data is exposed during decryption
Advantage: matching performance is unaffected, can use the same matching algorithms
Generally not sufficient for securing biometric data

34. Feature transformation
A transformation function is applied to the template
Transformation function derived from a password or random key
Can be invertible or non-invertible
Invertible
Security is derived from the secrecy of the password or key
Keys can be user-specific
Homomorphic encryption
Non-invertible
Equivalent to password hashing
More secure than invertible feature transformation
Challenging to create non-invertible transformation functions
Can adversely affect matching performance

35. Biometric cryptosystems
Biometric data is used to bind or generate cryptographic keys
Key binding: use biometric data to identify a key that was generated independently of the biometric data itself
Key generation: use biometric data to create a key
Biometric cryptosystems are an area of active research
Helper information that is publicly available is used to stabilize the system

36. Discussion There is no ideal method of biometric security
Many open challenges
Today, hybrids of multiple techniques are increasingly common
Research topics to consider:
Are there existing security methods that can be extended to biometrics?
How can we measure the security of a biometric template database?
Are there biometrics that are not compatible with existing security schemes?

37. Thank you!
Questions? or stop by BYENG 506