Koen Simoens, Stefaan Seys, and Bart Preneel 

K.U.Leuven ESAT/SCD – COSIC 

(Belgium) 

NIST IBPC 2010 

FP7 Integrated Project TURBINE (TrUsted Revocable Biometric IdeNtitiEs) 
• TURBINE project 

– TrUsted Revocable Biometric IdeNtitiEs 

– Privacy-enhanced solution for fingerprint biometrics 

– EU funded under FP7 

– http://www.turbine-project.eu 

• Different protection methods developed by 

– Sagem Sécurité (France), Philips Research Europe (the 

Netherlands), Gjøvik University College (Norway) 

• Evaluation tasks 

– Security testing: K.U.Leuven (Belgium – also legal evaluation) 

– Biometric performance testing: Gjøvik University College 

• This talk reflects to some extent the security and privacy

assessment of template protection techniques developed

in TURBINE 

TURBINE – NIST IBPC 2010 – Gaithersburg – March 2010 
• What is biometric template protection? 

• Why do we need it? 

• What do we expect (requirements)? 

• How can we achieve it (types)? 

• Which are the common pitfalls? 

• Are their fundamental principles? 

• Where to start with the evaluation? 

• How to compare results? 

• What do we need more? 

TURBINE – NIST IBPC 2010 – Gaithersburg – March 2010 
• The Meaning of Template Protection 

• Why More Research on Template Protection is

Needed 

• Evaluation of Template Protection 

• Objectives 

– Give intuition about fundamental principles 

• Design or analysis of new methods 

– Raise questions on how to evaluate 

• A common base for evaluation is needed 

TURBINE – NIST IBPC 2010 – Gaithersburg – March 2010 
FP7 Integrated Project TURBINE (TrUsted Revocable Biometric IdeNtitiEs) 
Biometric 

System 

Security 

Template 

Level 

System 

Level 

Procedural 

Level 

Component 

Level 

Hardware 

Level 

TURBINE – NIST IBPC 2010 – Gaithersburg – March 2010 
• Many threats (impersonation, linking, etc.) 

– Conclusion: do not store reference data in the clear 

• Current countermeasures 

– Encryption 

– Physical security 

– ... 

=> Complement with template-level protection 

• Motivation from a risk management perspective

(what-if analysis) 

– Physical protection may fail 

– Insider threats (trust assumptions no longer hold) 

– Desired renewability feature 

TURBINE – NIST IBPC 2010 – Gaithersburg – March 2010 
• Biometrics-only model: “cannot be lost/forgotten” 

– Assume no keys, passwords or smart cards for security 

– Possibly token as storage medium 

– Biometrics are secrets, but they are noisy 

• Classical data privacy schemes do not work 

• Different methods have been proposed: 

– Quantization schemes 

– Discrete schemes 

– Mixed quantization/discrete schemes 

– Cancelable biometrics 

– ... 

TURBINE – NIST IBPC 2010 – Gaithersburg – March 2010 
TURBINE – NIST IBPC 2010 – Gaithersburg – March 2010 

• Fuzzy schemes allowing noisy inputs 

– Public data helps to reliably extract bits 

– Hash extracted bits and store as reference 

– Error-correction to deal with noise 

– Implicit comparison: wrong result if distance > t 

– Classic example: 

• Fuzzy commitment (Juels & Wattenberg, CCS ‘99) 

• TURBINE pseudo identity model 

– ISO 24745 Biometric Template Protection 

Auxiliary Data (AD) 

+ 

Pseudo ID 

(secure reference) 
• Schemes secure in isolated setting 

– But reality... 

– Registered in multiple applications 

with the same biometric characteristic ... 

• Multiple databases with protected templates 

• We tend to forget 

• Different applications = different algorithms 

TURBINE – NIST IBPC 2010 – Gaithersburg – March 2010 
• Main goals of template protection 

– One-way transformation: irreversibility 

– Diversification: unlinkability and revocability 

– Maintain biometric performance! 

• Subtle issues 

– What does reversibility mean? 

• Reverse to enrolment sample or to other genuine/ 

impostor sample 

– Two-template irreversibility 

TURBINE – NIST IBPC 2010 – Gaithersburg – March 2010 
FP7 Integrated Project TURBINE (TrUsted Revocable Biometric IdeNtitiEs) 

“Fundamental” principles 
TURBINE – NIST IBPC 2010 – Gaithersburg – March 2010 

• Personal ideas/opinion 

Not all principles are yet fundamental 

– Unproven, but give intuition 

Valid for all types of template protection? 

Recall 

– Template-level protection complements 

protection at other levels 

•

•

•
TURBINE – NIST IBPC 2010 – Gaithersburg – March 2010 

• Isometric one-way transformation 

– If b is a biometric sample and {b’} its neighbours 

– Take b somewhere else, thus {b’} also 

– This is why cryptographic hashing doesn’t work 

There is no single transformation for all 

– Transformation is adjusted to enrolment sample 

– Side information depends on input 

• User-specific auxiliary data (public helper data) 

•
TURBINE – NIST IBPC 2010 – Gaithersburg – March 2010 

• What is leakage? 

– Information that reduces “uncertainty” about the 

enrolment sample 

– It becomes “easier to guess” 

– Entropy reduction 

• Fuzzy extractors (Dodis et al. EUROCRYPT 2004) 

• Adam Smith (Ph.D. Thesis 2004) 

• Only for discrete sources 

Where does it come from? 

– It is in the side-information 

– It is needed to compensate noise 

– Leakage is tolerated but should not be ignored

•
TURBINE – NIST IBPC 2010 – Gaithersburg – March 2010 

• Fuzzy commitment 

– Enrolment sample w, probe w’ 

– Offset v (translation preserves distance) 

– Decode v + b’ to c’ and verify if h(c’) = h(c) 

v+b’ 

b’ 

b 

t 
TURBINE – NIST IBPC 2010 – Gaithersburg – March 2010 

• Theoretically proven AD must leak information 

• Inverse code-offset from any codeword 

• Uncertainty is reduced (no actual bits leaked) 

• Position in square is revealed implicitly 
TURBINE – NIST IBPC 2010 – Gaithersburg – March 2010 

• Current schemes leak to much 

– More leakage than needed to correct errors 

– This is why cross-matching works for fuzzy 

commitment and some quantization schemes 

• Simoens et al. S&P 2009 

• Buhan et al. SPEED 2009 

• Can we improve? 

– Mathematical bounds (coding theory) 
TURBINE – NIST IBPC 2010 – Gaithersburg – March 2010 

• Completely different PIs and ADs 

• Cross-matching based on relative positions 

• Successful attack against fuzzy commitment 

w’ 

w 

v1 

v2 

c2 

c1 
TURBINE – NIST IBPC 2010 – Gaithersburg – March 2010 

• Leakage is unavoidable 

• Leak the same in different applications 

– This implies using the same algorithm 

– Impossible to maintain in practice? 

• If not, reverse two protected templates 

– Theoretical attack (Simoens et al. S&P 2009)

– Easy to see for discrete biometrics 

• Code-offsets, projection based 

– What about cancelable biometrics? 
TURBINE – NIST IBPC 2010 – Gaithersburg – March 2010 

•

•

•

•

Assume exactly same input is used 

– In practice enrolment samples are not equal 

Codewords c and d are from different codes

Subtract offsets to obtain v = v1 − v2 = c − d 

Solve mathematical problem 

– Find c and d, from the first and the second code, 

who’s difference equals v 

– If codes are not properly chosen, there is only one 

solution to this problem. 

v1 

v2 

d

c

v1 - v2 
TURBINE – NIST IBPC 2010 – Gaithersburg – March 2010 

• Linear 

– “matrix”, “offset”, “translate”, “rotate”, “XOR”(!) 

– Is not good (cf. cryptography) 

• Impact 

– Code-offsets 

• Linkability (cross-matching) 

• Two-template reversibility

– Transformation of point-based features (e.g. minutiae sets) 

• Correlation between minutiae is preserved 

• Non-linearity 

– Where to get it? 

– Conflicts with isometric input transformation 
TURBINE – NIST IBPC 2010 – Gaithersburg – March 2010 

• FAR attacks are inherent to biometrics

– Template-level protection requires additional measures 

– FAR attacks hurt more than you’d expect 

• Recover enrolment data from false accept 

• Some schemes allow a wider input range 

– Examples: projection-based schemes, superimposing 

minutiae subsets 

– Attempt to counter some of the above attacks 

– Curiosity in Template Protection 

• Theoretical increase in false acceptance rate? 

• Theory differs from practice 

– Evaluation requires working with real data 

– Distribution of binarized templates matters 
TURBINE – NIST IBPC 2010 – Gaithersburg – March 2010 

• Including hardware is out of scope in the 

biometrics-only model 

– But it works! At least, we think so... 

– Nicer properties 

• Information content (entropy) is limited 

– How many minutiae in a fingerprint? 

– What is the scanner resolution? 

– What is the range of the coordinates? 

– If you take into account noise tolerance? 

• Is it not all discrete? 

– Quantization schemes 

– In the end... all the same? 
FP7 Integrated Project TURBINE (TrUsted Revocable Biometric IdeNtitiEs) 
TURBINE – NIST IBPC 2010 – Gaithersburg – March 2010 

• Do you have a proper framework 

– To model/analyze ALL methods 

• E.g. fuzzy extractors for discrete biometrics 

– To set proper terms of reference 

• How to define and measure security properties? 

• What is it that you want? 

– Application requirements 

– Are you willing to trade between properties 

• E.g. irreversibility vs. unlinkability 

– Are the requirements realistic and needed in practice? 

• Get into the right mindset 

– Become a non-believer 
TURBINE – NIST IBPC 2010 – Gaithersburg – March 2010 

• Set clear adversary objectives 

– E.g. we want to compare/link protected templates 

• Test principles mentioned above 

– Where is the leaked information? 

– In which form is it leaked? 

– How much is leaked and can we use it? 

• How to rank different protection methods 

– Difficult without a unified framework 

– Already some consensus on security properties 

– Ongoing discussion in TURBINE and elsewhere 
TURBINE – NIST IBPC 2010 – Gaithersburg – March 2010 

• We need more advanced techniques 

– Non-linear methods 

• We need more formal approaches 

– Unified security notions 

– Less “we think/believe...” 

• We need provable security

– Cf. public-key cryptography: security proven 

under some number-theoretic assumptions 

• Are we ready for the first Template(-level) 

Protection Standard? 
FP7 Integrated Project TURBINE (TrUsted Revocable Biometric IdeNtitiEs) 

koen.simoens@esat.kuleuven.be 

http://www.turbine-project.eu 

This work is supported by funding under the Seventh Framework Programme of the European 

Union, Project TURBINE (ICT-2007-216339). All information is provided as is and no 

guarantee or warranty is given that the information is fit for any particular purpose. The user 

thereof uses the information at its sole risk and liability. The European Commission has no 

liability in respect of this document, which is merely representing the authors’ view. 
TURBINE – NIST IBPC 2010 – Gaithersburg – March 2010 

• Buhan, I., Breebaart, J., Guajardo, J., de Groot, K., Kelkboom, E. and, 

Akkermans, T. 2009. A quantitative analysis of crossmatching 

resilience for a continuous-domain biometric encryption 

technique. In Proc. Of the First International Workshop on Signal 

Processing in the EncryptEd Domain (SPEED 2009). 

• Dodis, Y., Ostrovsky, R., Reyzin, L., and Smith, A. 2008. Fuzzy 

extractors: How to generate strong keys from biometrics and other 

noisy data. SIAM J. Comput. 38, 1, 97-139. http://dx.doi.org/

10.1137/060651380 

• Juels, A. and Wattenberg, M. 1999. A fuzzy commitment scheme. In 

Proc. of the 6th ACM Conference on Computer and Communications 

Security - CCS '99, 28-36. http://doi.acm.org/10.1145/319709.319714 

• Simoens, K., Tuyls, P., and Preneel, B. 2009. Privacy weaknesses in 

biometric sketches. In Proc. of the 2009 30th IEEE Symposium on 

Security and Privacy, 188-203. https://www.cosic.esat.kuleuven.be/

publications/article-1205.pdf 

• Smith, A. D. 2004 Maintaining Secrecy when Information Leakage is 

Unavoidable. Doctoral Thesis. Massachusetts Institute of Technology.