© Fraunhofer IGD 

IBPC - Gaithersburg, 03 March 2010 – Xuebing Zhou 

Security Evaluation of Biometric Privacy 

Enhancing Techniques 

Xuebing Zhoua, Bian Yangb and Christoph Buscha 

aFraunhofer Institute for Computer Graphics Research IGD, Germany 

bGjovik University College, Norway
© Fraunhofer IGD 

! Motivation 

! Overview of Template Protection Techniques 

! Protection Goal and Threat Model 

! Examples of Security Evaluation 

! Fuzzy Commitment Scheme 

! Biohashing 

! Evaluation Framework 

! Conclusions 

Content 

IBPC - Gaithersburg, 03 March 2010 – Xuebing Zhou 
© Fraunhofer IGD 

! Privacy and Security Risks in Common Biometric System 

! Identity theft 

! Cross matching 

! Unchangeability

! Privacy and legislation 

! Template protection technique 

! It converts biometric data into multiple various references, from which it is 

infeasible and hard to retrieve the original information. 

! Biometric template protection is a very important supplement to improve 

security and enhance privacy protection of biometric systems. 

! New challenge: how to evaluate security and privacy enhancing ability of a 

template protection technique? 

Motivation 

•ISO/IEC CD 24745 Information technology - Security techniques - Biometric template protection

IBPC - Gaithersburg, 03 March 2010 – Xuebing Zhou 
© Fraunhofer IGD 

Reference Architecture for Template Protection Technique 

•J. Breebaart, C. Busch, J. Grave, E. Kindt: A Reference Architecture for Biometric Template Protection based on Pseudo Identities, in Proceedings BIOSIG 2008 

IBPC - Gaithersburg, 03 March 2010 – Xuebing Zhou 
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! Protected template 

! Pseudonymous Identifier : PI

! Auxiliary Data: AD

! Supplementary Data: SD

! Pseudonymous Identifier Encoder: 

! [PI, AD] = PIE(X, SD)

! Pseudonymous Identifier Recorder: 

! [PI’] = PIR(X’, AD, SD)

Template Protection Techniques* 

•ISO/IEC CD 24745 Information technology - Security techniques - Biometric template protection

IBPC - Gaithersburg, 03 March 2010 – Xuebing Zhou 
© Fraunhofer IGD 

! Transformation algorithms 

! Biometric Salting: 

! Biohashing: randomly converts features into bit strings 

! Biometric Encryption: whitening features in frequency space 

! Cancelable Biometrics: transformation of features or morphing of samples 

with non-invertible function 

! Biocrypto algorithms 

! Fuzzy commitment: cryptographic hashing (ordered set) 

! Fuzzy vault: secret sharing protocol (non-ordered set like fingerprint 

minutiae) 

State of the Art of Template Protection Techniques 

IBPC - Gaithersburg, 03 March 2010 – Xuebing Zhou 
© Fraunhofer IGD 

State of the Art of Template Protection Techniques 

Transformation Biocrypto 

Protected

template

(public)

PI Transformed feature/

sample 

Protected binary identifier

AD -- 

e.g. helper data or point 

set 

SD (secret) Specific parameters used 

in tranformation funtion Optional personal PW 

Comparison result Similarity score / 

Exact match Exact match 

IBPC - Gaithersburg, 03 March 2010 – Xuebing Zhou 
© Fraunhofer IGD 

! Sufficient security of PI:

! It is hard to guess the true binary identifier for an enroled subject in the 

biocrypto system 

! It is hard to reconstruct a sample or feature set yielding an identical 

transformation, which can successfully pass pseudonymous identifier 

verification 

! Low leakage of biometric information in PI or AD

! Little biometric information can be learned from PI or AD

! Good randomness of PI

! The true binary identifiers should be independent 

! No correlation between PIs (the transformed samples/features) 

! No personally identifiable information in AD

! AD is at best random 

Protection Goals 

IBPC - Gaithersburg, 03 March 2010 – Xuebing Zhou 
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! TM 1: Storage - A protected template of a subject is known 

! TM 2: Signal processing - Template protection algorithm is public (Kerckhoffs’ 

principle) 

! Linkage between databases - Stored protected templates of the same 

subject from different applications are known 

! System parameters are known 

! (Secret) supplementary data SD are known 

! The statistical properties of the biometric data are known 

! TM 3: Decision - A large biometric database is available 

Threat Models (TM) 

IBPC - Gaithersburg, 03 March 2010 – Xuebing Zhou 
! PI: h(S)

! AD: R, W

! System information: coding algorithm, coding parameters, e.g. Ls, Lc

© Fraunhofer IGD 

An Example of Fuzzy Commitment Scheme 

IBPC - Gaithersburg, 03 March 2010 – Xuebing Zhou 
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! TM 1: A protected template is known 

! Cryptographic security of PI = Ls bits (secret size) 

! Leakage of biometric information in PI and AD is neglectable

! TM 2: Template protection algorithm is public 

! Coding algorithm and coding parameters are known: 

! Cryptographic security of PI = Ls bits 

! Leakage of biometric information in AD = Lc-Ls bits 

! More entries of the same subject in different databases are known: 

! Identification ability of AD: FMR, FNMR or probability of identification for 

numerous ranks given R (R is indicating reliable bits in the binary vector) 

Security Evaluation of the Fuzzy Commitment Scheme 

IBPC - Gaithersburg, 03 March 2010 – Xuebing Zhou 
© Fraunhofer IGD 

! TM 2 (continued): Template protection algorithm is public 

! Coding algorithm, parameters and more entries of the same subject in 

different databases (combining leakage) 

! Cryptographic security of PIs: "(S1|W1,W2), "(S2|W1,W2)

! Security leakage: Ls-"(S1|W1,W2), Ls-"(S2|W1,W2)

! Leakage of biometric information in AD: Î(X1;W1,W2), Î(X1;W1,W2) 

! Statistical properties of binary biometric feature vector are known or partially 

known as well as the coding algorithm and coding parameters: 

! Cryptographic security of PI: "(S|W)

! Security leakage: Ls- "(S|W)

! Leakage of biometric information in AD: Î(X;W) 

Security Evaluation of the Fuzzy Commitment Scheme 

IBPC - Gaithersburg, 03 March 2010 – Xuebing Zhou 
© Fraunhofer IGD 

! Remarks: 

! A stream cipher is used in the fuzzy commitment scheme 

! Cryptographic security of PI = Uncertainty about X in W (H(X|W))

! The assumption that the public helper data W has zero leakage of S is true 

only in the case that X is identically independently distributed 

! The security leakage is allowed, if PI is cryptographically secure 

! Leakage of biometric information in AD can lead to leakage of PI: 

"(S1|W1,W2)#"(S1|W1). Because W2 is dependent on biometric 

information X2 and randomly generated S2, increasing leakage is caused 

by the correlation between W2 and X1

Security Evaluation of the Fuzzy Commitment Scheme 

IBPC - Gaithersburg, 03 March 2010 – Xuebing Zhou 
© Fraunhofer IGD 

! Any feature xi in x = {xi |i![1, ···, n]} can be 

binarized with m randomly generated 

thresholds t = {t j

i

| j! [1, ···, m], i![1, ···, n]} into 

b = {b j

i

| j! [1, ···, m], i![1, ···, n]}, where t j

i has 

the same distribution as xi. 

! Comparison based on Hamming distance 

between Biohashes. 

! Pseudonymous Identifier PI : b containing

m$n bits 

! Supplementary Data SD : t

! System parameter: m, n

An Example of Biohashing based on Scalar Randomization 

1 t1

0 t2

1 t3

1 t4

1 t5

0 t6

0 t7

xi

bi ti

IBPC - Gaithersburg, 03 March 2010 – Xuebing Zhou 
© Fraunhofer IGD 

! TM 1: A protected template (Biohashes) is known 

! --: without information about Biohashing algorithm, Biohashes mean nothing 

! TM 2: Template protection algorithm is public 

! The parameters of the algorithm are known: 

! Leakage of biometric information in PI: , where is the 

 cumulative probability of with e.g. the 95% confident interval of 

! Randomness of PI = ; for all users, the average 

 information rate of Biohashes is 0.72

Security Evaluation of the Biohashing Algorithm 

! !

ˆ f (xi

) = j bi

j

"

m

!

ˆ f (xi

) ±1.96

ˆ f (xi

)(1" ˆ f (xi))

m

!

m " H f (xi ( )) i=1

n

#

IBPC - Gaithersburg, 03 March 2010 – Xuebing Zhou 

!

ˆ f (xi

)
! TM 2 (continued): 

! The parameters of the algorithm and SD (binarization thresholds are 

compromised) are known 

! Leakage of biometric information 

! 

! X 

! If more entries of the same subject in different databases are given, more 

accurate estimation can be done 

! The parameters of the algorithm and the distribution of x are known

! Leakage of biometric information in PI: 

© Fraunhofer IGD 

Security Evaluation of the Biohashing Algorithm 

!

!

minbi =1 t { i

} "maxbi = 0 t { i}

max t { i

} "min t { i}

!

p ˆ (xi) = p ˆ (ti)

!

x ˆ i = f "1 j bi

j

#

m

$

%

&

&

'

(

)

)

IBPC - Gaithersburg, 03 March 2010 – Xuebing Zhou 
© Fraunhofer IGD 

! False acceptance attack: , where X and X’ are 

from different subjects. 

! FMR shows the average collision probability of biometric data 

! The probability of successful false acceptance attack might be different than 

FMR. For example in Doddington’s biometric menagerie: 

! Lambs: FMRL>FMR 

! Wolves: FMRW<FMR

! In contrast to randomly generated identifiers, where all the identifiers are 

equally probable, pseudonymous identifiers generated from lambs/wolves 

characteristics are not equally probable 

! False acceptance is inherent prosperity of biometric system and false 

acceptance attack is not avoidable. However it can be controled with e.g. time 

out policy 

Threat Model 3 

!

P PI ( X = PIR(X',AD,SD)) = FMR

IBPC - Gaithersburg, 03 March 2010 – Xuebing Zhou 
© Fraunhofer IGD 

Evaluation Framework 

IBPC - Gaithersburg, 03 March 2010 – Xuebing Zhou^ 
© Fraunhofer IGD 

! All the requirements on template protection should be measured quantitatively 

with evaluation function 

! From the security and privacy point of view, leakage on biometric information 

should be limited 

! to AD of protected template in biocrypto algorithms 

! even when the SD of transformation algorithms is compromised 

! The likelihood to reconstruct true biometric data from protected template is 

dependent on SD chosen. An efficient template protection algorithm should 

have homogenous security for all the subjects and possible SD.

Remarks on Evaluation Framework 

IBPC - Gaithersburg, 03 March 2010 – Xuebing Zhou 
© Fraunhofer IGD 

! A general evaluation framework according to 3 main threat models was 

developed. 

! Depending on the threat model, the measurable security evaluation functions 

are defined 

! It is necessary to measure privacy leakage of protected template including 

leakage of biometric information and any other personal identifiable 

information. Privacy leakage causes linkability and can give rise to further 

security problem 

! In order to improve confidentiality of authentication and integrity of protected 

templates, cryptographic technique such as digital signature and encryption 

can be combined with template protection algorithms. 

Conclusions 

IBPC - Gaithersburg, 03 March 2010 – Xuebing Zhou 
© Fraunhofer IGD 

 Xuebing Zhou

Fraunhofer Institute for Computer Graphics

 Research IGD 

Fraunhoferstraße 5 

64283 Darmstadt 

Germany 

Tel +49 6151 155 – 537 

Fax +49 6151 155 – 499 

xuebing.Zhou@igd.fraunhofer.de 

www.igd.fraunhofer.de

Thank you for your attention

IBPC - Gaithersburg, 03 March 2010 – Xuebing Zhou 
© Fraunhofer IGD 

Threats in the Biometric System 

•ISO/IEC CD 24745 Information technology - Security techniques - Biometric template protection

IBPC - Gaithersburg, 03 March 2010 – Xuebing Zhou 

Data 

Capture

System

Signal 

Processing

Subsystem

Comparison

Subsystem

Data Staorage

Subsystem

Decision

Subsystem