A Generalized Framework for 

Privacy and Security Assessment of 

Biometric Template Protection

Xuebing Zhou 

CASED - Center for Advanced Security Research Darmstadt

Hochschule Darmstadt 

Gaithersburg, March 09, 2012 
 

 

 

 

 

Content 

§Biometric template protection 

§ How to assess biometric template protection

the systematic evaluation framework 

§Assessment of different systems 

§ Conclusions 

§Future work 

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Biometric Systems 

§Privacy and security risks 

§ Identity theft 

§ Unchangeability 

§ Cross matching 

§ Harm of privacy 

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Biometric Systems 

§Privacy and security risks 

§ Identity theft 

§ Unchangeability 

§ Cross matching 

§ Harm of privacy 

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Biometric Template Protection 

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Biometric Template Protection 

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Biometric Template Protection 

PT1

PT2

PT3

PT4

PT

PT

5

n

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Biometric Template Protection 

§Properties of

protected templates (PT) 

PT1

PT2

PT3

PT4

PT

PT

5

n

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Biometric Template Protection 

§Properties of

protected templates (PT) 

§ Irreversibility 

PT1

PT2

PT3

PT4

PT

PT

5

n

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Biometric Template Protection 

§Properties of

protected templates (PT) 

§ Irreversibility 

§ Robustness 

PT1

PT2

PT3

PT4

PT

PT

5

n

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Biometric Template Protection 

§Properties of

protected templates (PT) 

§ Irreversibility 

§ Robustness 

§ Diversity 

PT1

PT2

PT3

PT4

PT

PT

5

n

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Biometric Template Protection 

§Properties of

protected templates (PT) 

§ Irreversibility 

§ Robustness 

§ Diversity 

§ Unlinkability 

PT1

PT2

PT3

PT4

PT

PT

5

n

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State of the Art of Template Protection 

§Transformation-based algorithms 

§Biometric salting 

§ Biometric encryption [Soutar99,

Savvides04, Takaragi07 etc.] 

§ Biohashing [Teoh04, Teoh09, Ao09 etc.] 

§Cancelable biometrics [Ratha01,

Zuo08, Bolle09 etc.] 

§Biometric cryptosystems 

§Fuzzy extractor [Dodis03] 

§ Fuzzy commitment scheme [Juels99] 

§ Helper data scheme [Tuyls04] 

§ Fuzzy vault scheme [Juels02] 

§Quantization index modulation [Linnartz03, 

Buhan08] 

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Biometric Template Protection 

ISO Architecture*

v 

 

 

 

 

 

 

 

§ Pseudonymous Identifier Encoder (PIE): [PI, AD] = PIE(M), M is observed biometric data in 

enrolment 

§ Pseudonymous Identifier Recorder (PIR): [PI’] = PIR(M’, AD), M’ is probe biometric data 

§ Pseudonymous Identifier Comparator (PIC): v = PIC (PI , PI’), v is comparison result 

§ Stored protected template [PI, AD], where PI is pseudonymous identifier and 

AD is auxiliary data 

* ISO/IEC 24745 (2011) Information technology - Security techniques - Biometric Information protection 

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How to Assess Template Protection 

§Protection goals - Evaluation criteria 

§ Security of PI: Hardness to find an M* (“pre-image” of PI), which can pass 

PI- verification process 

§ Privacy protection ability: 

§ Irreversibility: Hardness to find an M*, which is very close to the original M 

§ Privacy leakage: Information about M contained in protected templates 

§ Unlinkability: 

§Cross matching: Personal identifiable information contained in

protected templates 

§Leakage amplification: Additional information about M or pre-image of PI gained

when combining protected templates of the same subject 

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How to Assess Template Protection 

§Threat models - description of an adversary 

§ Naive Model: Adversary has no information about the system 

§ Advanced Model: Adversary has full knowledge of the algorithm 

(Kerckhoffs’ principle) and properties of biometric data 

§ Collision Model: Adversary owns a large amount of biometric data

and can exploit inaccuracies of the biometric system 

§ Distribution of biometric features 

§ Important a priori information for an adversary 

§ Essential for security and privacy assessment 

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How to Assess Template Protection 

Evaluation framework 

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How to Assess Template Protection 

§Definition of security: 

§Let A(AD, PI)=[M ́, PI ́] be a reconstruction function, where PI ́=PIR(M ́, AD). 

TA is the computational time required in one reconstruction and 

n is the average number of reconstructions needed to get a [M ́, PI ́] such 

that PIC(PI,PI ́)=1 for a positive authentication result. 

§Then, a template protection algorithm is (Τ, ε)- secure, if for all A 

TA ≥ Τ

log2 n ≥ ε

 

v 

 

 

 

 

 

 

 

 

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T A ≥ Τ

log2 n ≥ ε

How to Assess Template Protection 

§Definition of security: 

§Let A(AD, PI)=[M ́, PI ́] be a reconstruction function, where PI ́=PIR(M ́, AD). 

TA is the computational time required in one reconstruction and 

n is the average number of reconstructions needed to get a [M ́, PI ́] such 

that PIC(PI,PI ́)=1 for a positive authentication result. 

§Then, a template protection algorithm is (Τ, ε)- secure, if for all A 

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T A ≥ Τ

log2 n ≥ ε

 

 

 

 

 

 

T A ≥ Τ

log2 n ≥ ε

How to Assess Template Protection 

§Definition of security: 

§ Let A(AD, PI)=[M’, PI’] be a reconstruction function, where PI’=PIR(M’, AD). 

TA is the computational time required in one reconstruction and 

n is the average number of reconstructions needed to get a [M’, PI’] such 

that PIC(PI,PI’)=1 for a positive authentication result 

A template protection algorithm is (Τ, ε)- secure, if for all A 

§Definition of privacy: 

§

§Let A(AD, PI)=[M ́, PI ́] be a reconstruction function, where PI ́=PIR(M ́, AD). TA 

is the computational time required in one reconstruction; for a given threshold t, 

n is the average number of reconstructions needed to get a [M ́, PI ́] such that 

for a distance function dist(M, M ́)<t 

§A template protection algorithm is (t, Τ, ε)- preserving, if for all A 

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Assessment of Different Protected Systems 

§The fuzzy commitment scheme for 3D face recognition 

§The fuzzy commitment scheme for iris recognition 

§The fuzzy vault algorithm for fingerprint recognition 

W Secret 

S XOR 

X

Keystream 

Generator 

Error 

Correction 

Encoder 

C XOR S’

X’

Error 

Correction 

Decoder 

C’

Encoder Decoder

Minu%ae(Informa%on.

 

 

 

 

 

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Naive ModelAdvanced Model Collision Model 

System LS ε=LS-1 Τ ε Τ ε=-log2 (FAR)

FAR@FRR Ranking 

3D Face Fuzzy 

Commitment 71 bit 70 O(1) 11.13 O(1) 6.48 

1.12%@19.97% 

Iris Fuzzy 

Commitment 72 bit 71 O(1) 14.25 O(1) 7.41 0.59%@22.74% 

Fingerprint

Fuzzy Vault* 128 bit 127 O(1) 34.54 O(n log2(n)) 13.29 0.01%@9% 

Assessment of Different Protected Systems 

§Security assessment 

* “Fingerprint-Based Fuzzy Vault: Implementation and Performance”, Nandakumar, Jain and Pankanti, IEEE Trans. on Info. Forensics and Security, 2007 

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Assessment of Different Protected Systems 

§Privacy protection ability in the advanced model: 

§ High privacy leakage, which can cause cross matching and leakage amplification 

§ Irreversibility is measured with the privacy definition for t=0. It shows computational 

complexity to retrieve the original biometric features 

System LS Privacy leakage 

Irreversibility 

ε Τ

3D Face Fuzzy 

Commitment 71 bit 77.5 bit 74.2 bit O(1)

Iris Fuzzy 

Commitment 72 bit 4311 bit 14.25 bit O(1)

Fingerprint

Fuzzy Vault* 128 bit 892.59 bit 34.54 bit O(n log2(n))

* “Fingerprint-Based Fuzzy Vault: Implementation and Performance”, Nandakumar, Jain and Pankanti, IEEE Trans. on Info. Forensics and Security, 2007 

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Assessment of Different Protected Systems 

§ Unlinkability in the advanced model: 

§ Cross matching is a serious problem 

§ It should be avoided to use any personal identifiable information in the systems 

§ Additionally, the privacy leakage is unavoidable in these system due to error tolerance,

but it should be minimized 

System Cross matching Leakage Amplification 

3D Face Fuzzy Commitment 

EER=5% no feasible attack yet 

Iris Fuzzy Commitment 

EER =16.34% 

Fingerprint Fuzzy Vault* no assessment in the paper no assessment in the paper 

* “Fingerprint-Based Fuzzy Vault: Implementation and Performance”, Nandakumar, Jain and Pankanti, IEEE Trans. on Info. Forensics and Security, 2007 

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Conclusions 

§The framework is useful to detect vulnerabilities of the existing

algorithms 

§The framework enables rigorous assessment, which is important and

necessary for the development of template protection 

§All the protection goals need to be taken into account 

§Threat models are the important prerequisites. Security and privacy 

protection ability of a system can be overestimated, if unrealistic 

assumption is made 

§ Unique and measurable metrics such as the metrics used in the

security and privacy definitions, are necessary for ranking of different

algorithms 

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Future Work 

§ Universal and constructive criteria, which can guarantee security and

privacy performance of template protection 

§An extended evaluation including both security and recognition

performance 

§Benchmarking and certification for template protection 

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References 

§ Zhou, Xuebing: “Privacy and Security Assessment of Biometric Template Protection”, PhD thesis,

Technische Universität Darmstadt, Germany, 2011 

§ Zhou, Xuebing; Kuijper, Arjan; Busch, Christoph: Cracking Iris Fuzzy Commitment In: IEEE the

International Conference on Biometrics (ICB 12), 2012 

§ Zhou, Xuebing; Kuijper, Arjan; Veldhuis, Raymond; Busch, Christoph: Quantifying Privacy and

Security of Biometric Fuzzy Commitment In: IEEE the International Joint Conference on Biometrics 

(IJCB 11), 2011 

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Xuebing Zhou

Post doc | Department Secure Services

CASED 

Mornewegstr. 32 Telefon +49(0)6151 16 75181

64293 Darmstadt/Germany Fax +49(0)6151 16 4825

xuebing.zhou@cased.de www.cased.de

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