Transcript Presentation title

IST-2002-506883
Secure contracts signed by mobile Phone
SecurePhone:
a mobile phone
with biometric authentication
and e-signature support
for dealing secure transactions
on the fly
Jacques Koreman, ISK
What is a SecurePhone?
• Mobile communications device that enables
exchange of text/audio documents during a phone
call to draw legally valid transactions.
• Secure communication uses e-signing in a system
using front-edge technologies (wireless networking,
double-key cryptography).
• Biometric recogniser enables strong authentication
by comparing live biometric features with models
trained on enrolment data.
• Biometric authentication grants access to built-in
e-signing facilities integrated on the PDA/SIM.
Dept. of Telematics, NTNU, 07.12.2006
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Presentation Outline
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SecurePhone concept and use
Project aim 1: secure exchange
Project aim 2: biometric recognition
– on PDA, high performance
– on SIMcard, proof-of-concept
Conclusion
Outlook
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Project aim 1: secure exchange
Secure PKI (personal key infrastructure)
• Public key encryption technology is used for
e-signature, i.e. to enforce data integrity and
non-repudiation
• Standard e-signature certificates and proce-
dures are used for certificate verification and
management, so documents e-signed by
means of the SecurePhone have the same
legal validity as documents e-signed by other
means.
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Secure architecture
• SIM card used as a tamper-proof device for storage
of private key ("strong signature" if the corresponding digital certificate is issued by a valid CA).
• Storage of biometric templates and matching
̶ on a TTP server (ToS/MoS): not implemented
̶ on the PDA/host (ToH/MoH): implemented
̶ on the SIM card (ToC/M0C): proof-of-concept
• Only ToC+MoC meets requirements on
̶ security
̶ privacy and user acceptance
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User scenario
• During a phone call, two SecurePhone end users
(actors) agree on drawing a distance contract.
• One actor (proposer) sends an e-contract (text/audio
file) to the other actor (endorser) .
• In case of text files, the e-contract can be interactively
modified and transmitted back and forth until a formal
agreement on its contents is reached
• To finalize the m-transaction, the endorser e-signs and
sends the e-contract as evidence of formal acceptance
of the terms contained therein.
• Depending on the e-contract type, the proposer may
also be requested to e-sign the e-contract.
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Project aim 2: biometric verification
• Local authentication unlocks cryptographic functions
by accessing the private key securely stored on the
SIM card
• PIN- or password-based authentication admissible
yet weak  unsatisfactory for security-critical
applications (e-commerce, e-health, e-government)
• Local authentication strengthened in order to
increase user’s trust in the system by combining
̶ WYK: a token only the user knows (signature)
̶ WYH: a token the user holds (PDA with SIMcard)
̶ WYA:biometric identity
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miss probability (%)
false rejection rate (%)
Password are not reliable enough
false acceptance rate (%)
false alarm probability (%)
DET curve
(detection error tradeoff)
Alvin Martin et al. (1997). The DET curve in
assessment of detection task performance,
www.nist.gov/speech/publications/
"Open, Sesame!"
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Fusion of multiple modalities
The Incredibles
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Properties of biometrics
Two types of biometrics:
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physical
behavioural
variation!
(due to repetitions, sessions, channel, background noise)
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Why face, voice and signature?
These biometrics have a high user acceptance:
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chosen biometrics are user-friendly
very short enrolment session
capture does not require special hardware
difficult to impostorise all three modalities
biometric profile remains local, guaranteeing privacy
processing of the biometric data also local (privacy)
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User verification procedure
• User requests identity verification
• PDA requests user to
• read prompt: face + voice
• sign signature
• Feature processing applied to each
modality
• Comparison to biometric profiles of
client and impostors
• Verification decision: accept/reject
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start/stop
Press to start/stop speaking
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Multi-modal biometric verification
face
voice
signature
preprocessing
preprocessing
preprocessing
modelling
modelling
modelling
fusion
client & impostor
joint-score models
reject
accept  release private key
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Voice verification
• Fixed 5-digits prompt – conceptually neutral, easily
extendable
• 22 KHz sampling
• Online energy based non-speech frame removal
• MFCCs with online CMS and first-order time difference
features (10-ms frames)
• Features modelled by 100-Gaussian GMM, with UBM for
model initialisation and score normalisation
• Training on data from 2 indoor and 2 outdoor
recordings from one session, testing on similar data
from another session
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Face verification
• Static face recognition using 10 grey-scale images
selected at random, 160x192 pixels
• Histogram equalisation and z-score normalisation of
features
• Haar LL4 (or LH4) wavelet features – fast to compute
(esp. compared to facial definition parameters)
• Features modelled by 4-Gaussian GMM, with UBM for
model initialisation and score normalisation
• Training on data from 2
indoor and 2 outdoor
recordings from one
session, testing on similar
data from another session
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Signature verification
• Shift normalisation, but no rotation or scaling
• 2D coordinates (100 Hz) augmented by time difference
features, curvature, etc. – total 19 features
Note: no pressure or angles available, since obtained
from PDA’s touch screen, not from writing pad
• Fast to compute
• Features modelled by 100 Gaussian GMM pdf – UBM
used for model initialisation and score normalisation
• Training and testing on data from one session
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Fusion
• For each modality S(i) = log p(Xi|C) - log p(Xi|I),
latter for casual impostors, only signatures
impostorised by experts
• Best LLR score fusion was obtained for
GMM scores modelling, i.e. modelling both
client and impostor joint score pdf’s by
diagonal covariance GMMs:
Fused-score = log p(S|C) - log p(S|I)
Tests on PDAtabase
• Database recorded on Qtek2020 PDA for realistic
conditions (sensors, environment)
• 60 English subjects: 24 for UBM, 18 for g1, 18 for g2.
Accept/reject threshold optimised on g1, then evaluated
on g2, and vice versa
• Video (voice + face): 6 x 5-digit, 10-digit and phrase
prompts; 2 sessions, with 2 inside and 2 outside
recordings per session
• Signatures in one session, 20 expert impostorisations each
• Virtual couplings of audio-visual with signature data
• Automatic test script to test many possible configuration
• User just provides executables for feature modelling,
scores generation and scores fusion
Performance on PDA
DET curves for prompts T1 (5 digits, left), T2 (10 digits, middle) and
T3 (short phrases, right) in PDAtabase
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Performance on PDA
EER
R=1
R=0.1
R=10
WER (FAR/FRR)
WER (FAR/FRR)
WER (FAR/FRR)
T1 2.39 2.40 (1.57/3.24) 1.87 (4.97/1.56) 1.02 (0.43/6.95)
T2 1.54 1.60 (0.89/3.32) 1.37 (3.05/1.20) 0.63 (0.25/4.37)
T3 2.30 2.37 (1.61/3.14) 2.03 (4.54/1.78) 0.92 (0.38/6.34)
Fusion results (% WER, FAR and FRR) for 3 prompt types
T1 = 5 digits, T2 = 10 digits, T3 = short phrases) in the
PDAtabase, at three critical points in the DET curve
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Implementation constraints
• PDA main processor allows speech preprocessing in
real time for 22 kHz signals
Note: signal directly from mic, therefore > 8 kHz
• Only data on the SIM card is secure, so all biometric
models must be stored and processed on the SIM,
which has very limited computational resources
• SIM model storage limited to 40K: text-dependent
prompts
Note: text-independent prompts or varied textdependent prompts are more secure, but would
require 200-400K
• Enrolment can use only one short indoor session
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Performance on SIMcard
• SIM processor very slow: single verification 53 min.!
• Most time goes to voice and signature processing:
large #frames and models with many Gaussians.
• Drastic measures needed: global processing.
• By using means and standard deviations across all
frames in the utterance/signature, the number of
frames is reduced to one.
• Simpler data require few mixtures for GMM modelling
• Single verification now under 1 second, but
performance for T1 is now 10.5% EER
 proof-of-concept, but performance must be improved!
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Conclusion
Deal secure m-contracts during a mobile phone call
• secure: PKI with private key stored on SIM card
• dependable: multi-modal: voice, face, signature
• user-friendly: familiar, intuitive, non-intrusive
• flexible: legally binding text/audio transactions
• low-cost: no special hardware needed
• dynamic: mobile (anytime, anywhere)
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Outlook
• In FP7, many opportunities for security and trust
projects
• Security and trust do not only depend on the
communication channel
• but also on the identity of the communication partner
• for which biometric recognition can be used
If you see a role for biometric recognition in your
proposal, do not hesitate to contact me!
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Secure contracts signed by mobile Phone
IST-2002-506883
http://www.secure-phone.info
Please take my business card or e-mail me at
[email protected]
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