1. Coexistence Among Cryptography and Noisy Data Theory and Applications
Alawi A. Al-Saggaf, PhD.
King Fahd University of Petroleum and Minerals,
28th April, 2014

2. Motivations for Current Research

3. Motivations for Current Research
The bad news about Password and smart card breaches:
Password may be forgotten, easy to guess, difficult to remember.
Passwords cracking (such as John the Ripper) easily to defeat the hash value of the password.
Smart cad may be lost, stolen, easy to share.

4. Who Are You?

5. Motivations for Current Research (Cont’d)

6. Why Biometrics?

7. Robustness
Security Level
Method

8. Usefulness
The Future of Biometrics Market Research Report

9. Trends in biometric systems' deployment in the United States (2003)*
*Frost and Sullivan. U.S. Biometric Network Authentication Markets, 2004.

10. Biometrics Template Attacks

11. Biometrics Templates Attacks
Replacing Template
Tempering Template
Stolen Template

12. Biometrics is a Noisy Data
Same
Person
h( ……)
h( ……)

13. Mathematical framework For Coexistence among Cryptography and Noisy Data

14. Select security parameter k∊K
Generate crisp PK
Fk :g(M)×X→E
Encode the committed message m: g(m)=c
Witness chosen randomly
x∊RX
Fuzzy PK
F:g(M)×X→Y
Fuzzy Encryption
y=(Fk(c,x) ,x-c)=(ε,δ)
If (t<t3)
Apply error correction f(c’)=f(x’- δ)
Crisp Encryption
ε’ =Fk(f(c’) , (δ + f(c’) ))
Cd(ε’ )=1
Yes No
Wait
Reveal x’ to B
B act g-1(f(c’))=m
Error message t1 t2 t3
Fd(f(c’))=1 y
Comm algorithm
Party Ted: Setup phase
Party A: Commit phase
Setup algorithm
Open algorithm
Party B: Open phase

15. Security Analysis

16. Bound derivation for hiding property
Theorem 5.1: Suppose that (witness space) and (error correcting code set) are two independent random variables over the same sample space , and let be a random variable (difference vector) obtained by “exclusive OR” of elements of and Then the probability that an attacker is able to compute either or from the difference vector is no more than , where is the size of the error correcting code

17. Bound derivation for Statistical hiding property
Theorem 5.2: For any , let be a fuzzy public key. Then, an the proposed scheme based on is and the value of is always computed as: For and

18. Bound derivation for computational binding property
Theorem 5.3: For any , let be a fuzzy public key. Then, the proposed scheme based on is and the value of is always computed as:

19. Applications

20. Crisp encryption algorithm
1. Secure Biometrics System
Enrollment Procedure
Authentication procedure
Iris biometric
Choose a codeword c
Fuzzy Encryption
Crisp encryption algorithm
Encryption
Concealing algorithm
Difference vector δ
Retrieve algorithm
Iris
extraction
Iris biometric input B
Iris
extraction 20

21. key generation Procedure
2. Retrieve cryptographic key from biometrics template
Registration Procedure
key generation Procedure
Fuzzy Encryption
Difference vector
Iris code extraction
Retrieve
Iris code extraction
Encoding
Encryption
Decode
Cryptographic key
Encryption
Yes Is
Cryptographic key
generated No
Error message

22. PW PW 3. Biometrics based Remote User Authentication using Smart Cards
Registration protocol
Registration Center PW ♥
Alice
Logon protocol
Server ♥
Authentication PW

23. Thank you