Template Protection Testing Workshop Draft Agenda and Background Material for Discussion 

Satellite Workshop II – Template Protection Testing

Friday, March 5, 2010, 9am – 11am

NIST, Gaithersburg, MD

Admin Building 101, Employee Lounge

Point of Contact: Elaine.Newton@nist.gov

Draft Agenda

1. Welcome and Introductions – Elaine Newton, NIST

2. Template Protection Background – “Security and Privacy in Biometric Systems ‐

The purpose of Biometric Encryption,” Tom Kevenaar, priv‐ID

3. Panel on Testing of Template Protection Schemes

Moderator: Elaine Newton, NIST

Panelists:

Terry Boult, UCCS

Christoph Busch, Fraunhofer

Jean‐Christophe Fondeur, Sagem

Tom Kevenaar, priv‐ID

Nalini Ratha, IBM

Others TBD

• Presentations on “NIST Biometric Authentication Testing, Data, and

Principles” and “Measures and Trade‐offs of Biometric Template

Protection Schemes” – Elaine Newton, NIST

• Reaction from Panelists

• Discussion on How to Work with the TURBINE Project

• Attendee Questions and Comments

4. ISO 24745 Biometric Template Protection – Christoph Busch, Fraunhofer

5. Twiki Set‐up – Ross Micheals, NIST

6. Wrap‐up – Newton and Micheals from NIST

Background for Panel Discussion

NIST Special Publication 800-63 contains guidelines for remote authentication over open 

networks. Per an OMB memo (M-04-04), NIST SP 800-63 provides the technical requirements 

for meeting four levels of authentication assurance for remote access to government systems. 

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Template Protection Testing Workshop Draft Agenda and Background Material for Discussion 

These four qualitative levels range from little or no confidence in the claimant’s identity 

(assurance level 1) to very high confidence in the asserted identity’s validity (assurance level 4). 

Since its publication, this document has been referenced and adopted widely by non-USG 

organizations, and a version is currently being standardized by ISO and ITU-T. 

NIST SP 800-63 was first published in 2006 and currently being revised. Both the original and 

the draft revision make nearly identical statements regarding the use of biometrics for remote 

authentication (bold type added here for emphasis): 

“Biometrics do not constitute secrets suitable for use in the conventional remote 

authentication protocols addressed in this document. In the local authentication case, where 

the Claimant is observed and uses a capture device controlled by the Verifier, authentication does 

not require that biometrics be kept secret.” 

“Biometrics are unique personal attributes that can be used to verify the identity of a person who 

is physically present at the point of verification. They include facial features, fingerprints, DNA, 

iris and retina scans, voiceprints and many other characteristics. This publication recommends 

that biometrics be used in the registration process to later prevent a Subscriber who is in fact 

registered from repudiating the registration, to help identify those who commit registration fraud, 

and to unlock tokens. Biometric characteristics are not recommended for use directly as tokens in 

this document.” 

This document contains guidelines for identity management problems for each of the four levels 

of assurance, including “token and credential revocation and destruction.” 

Biometrics are not secret; nor are they easily destroyed and re-issued, unlike authentication 

factors such as passwords or smartcards with digital certificates. The ability to detect a live, 

unspoofed biometric sample at a sensor is a critical component of the integrity of biometrics for 

reliable authentication. Short of an attendee being present at the point of collection, liveness 

detection methods that can be independently evaluated could potentially be applied to aid the 

security of remote authentication over open networks. Depending upon the training and efficacy 

of personnel overseeing the authentication process, attended applications may also opt to employ 

liveness detection methods. 

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Template Protection Testing Workshop Draft Agenda and Background Material for Discussion 

As the use of biometrics becomes more wide-spread, the inability to destroy the factor and reissue a new digital identity could present heightened security weaknesses for organizations and 

compromise the ability of individuals to maintain confidentiality. The use of a common pool of 

developed biometrics, such as fingerprints, faces, and irises, could give rise to the ability to link 

data across domains. Imagine using the using the same password to log onto your personal email; 

your work email with sensitive, proprietary, and/or classified data; your online banking 

applications; your online travel agent; and all the websites you log into to make purchases or read 

news articles. Imagine not changing that password every 90 days, and imagine that every system 

administrator, professional to amateur hacker, curious intern, disgruntled former employee, etc. 

knew that this was the case. This would be analogous to wide-spread use of biometrics as an 

authentication factor without implementing measures to combat their weaknesses. 

Methods of protecting biometric data to solve this problem – to renew, revoke, or cancel the 

credential – are an active area of research and development. Here they will be generally referred 

to as template protection algorithms. The goal of these algorithms is to produce templates for an 

individual that accurately match, while severing the link to the individual’s. 

Current related NIST testing 

NIST has been conducting evaluations of fingerprint, face, iris, and speaker recognition 

algorithms to measure their accuracy. These evaluations largely report one-to-one matching error 

rates – false match and false non-match rates for algorithms submitted by companies and 

universities. NIST has the largest sequestered databases for independent testing of these 

modalities. 

For the case of remote authentication, authentication factors which are passed through a network 

would be encrypted to protect the secret information being communicated. NIST standards and 

guidelines published in FIPS 140-2 and SP 800-63 specify how this can be done. To support 

validation of products, NIST partners with the Communication Security Establishment Canada 

(CSEC) to run two programs: Cryptographic Algorithm Validation Program (CAVP) and 

the Cryptographic Module Validation Program (CMVP). 

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Template Protection Testing Workshop Draft Agenda and Background Material for Discussion 

Potential Measures of Template Protection for Remote Authentication 

The suitability of template protection algorithms for remote authentication could be comprised of 

three measurements: 

1. Authentication Accuracy 

a. This is the traditional measure of error rates from one-to-one comparisons, 

producing false match and non-match rates (and true match and non-match rates) 

that can be plotted on Receiver Operating Characteristic (ROC) curve. 

b. Measure: True Match Rate (how often transformed enrollment of Alice matches 

transformed capture of Alice at another time) 

i. Must be measured at a specific point on the ROC curve, at a false match 

rate that can be supported by the size of the data set 

2. De-Identification 

a. Face De-Identification was defined by Newton, Sweeney, and Malin1

 as 

“Let H and Hd be face sets, Γ∈H, Γd∈Hd, f:H→Hd be a function that 

attempts to conceal the identity of the subject of the original face image; 

and, f(Γ) = Γd but Γ≠Γd (element-wise). f is termed face de-identification 

(“de-identification”, “de-identification function”). Γd is a de-identified 

image.” 

They also define Effective De-Identification2

 as 

“Let H be a person-specific face set; Hd be a face set; f:H→Hd be the 

transformation function used in face de-identification, such that f(Γ)=Γd 

where Γ∈H and Γd∈Hd; g be a face identification relation g:Hd→H; and, C 

be a provable claim about f’s ability to restrict face identification (or face 

recognition) by g. The function f provides effective de-identification with 

respect to C and f is said to be effective. If f1 and f2 are effective with 

respect to the same C, then f1 and f2 are considered equally effective with 

respect to C.” 

While Newton et al. developed de-identification methods to apply to 

original biometric data to enable privacy-preserving data sharing (as 

opposed to a priori transformation at the sensor), the definitions for de1

 E.M. Newton, L. Sweeney, B. Malin. “Preserving Privacy by De-identifying Facial Images.” in IEEE 

Transactions on Knowledge and Data Engineering, February 2005, 17(2). 2

 Ibid. 

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Template Protection Testing Workshop Draft Agenda and Background Material for Discussion 

identification and effective de-identification can be generalized and used 

here to describe the goal of template protection algorithms, as follows: 

Let H be a person-specific biometric set; Hd be a biometric set; f:H→Hd be 

the transformation function used in biometric de-identification, such that 

f(Γ)=Γd where Γ∈H and Γd∈Hd; g be a biometric identification relation 

g:Hd→H; and, C be a provable claim about f’s ability to restrict biometric 

identification (or biometric recognition) by g. The function f provides 

effective de-identification with respect to C and f is said to be effective. 

b. Measure: False Non-Match Rate 

i. How often original of Alice’s biometric does not match transformed 

template of Alice 

ii. The FNMR Must be measured at a specific point on the ROC curve, at a 

false match rate that can be supported by the size of the data set 

3. Security Strength 

a. Preimage resistance – It should be very difficult to recover the original template 

or image from a protected template. 

b. Measure: Entropy, bits. 

Figure 1 shows the three measures, each increasing as you move away from the origin. 

Algorithms that are able to perform further from the origin would be considered better than those 

closer to the origin. 

Figure 1: 3-dimensions of template protection algorithm performance 

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