Biometrics and the Cloud

Transcription

1 Biometrics and the Cloud CWI-CTiF Workshop on "Cloud communication and applications" Zheng-Hua Tan Aalborg University, Denmark CWI-CTiF Workshop 2011, Copenhagen 1

2 Outline Cloud security Biometrics Voice biometrics Face biometrics Biometrics for the cloud Biometics in the cloud Summary CWI-CTiF Workshop 2011, Copenhagen 2

3 Cloud security Identity management and access control are fundamental functions for secure cloud communication. Simplest form: a user ID and password; more robust one uses technology such as smart cards or biometrics. Identification: the act of a user claiming an identity to a system, in the form of a username or user logon ID. Authentication: verifícation of the user s claimed identity, based on: Something you know, e.g., a PIN or password Something you have, e.g., an ATM card or smart card Something you are (physically), e.g., a fingerprint Krutz and Vines, Cloud security: a comprehensive guide to secure cloud computing, Wiley, 2010 CWI-CTiF Workshop 2011, Copenhagen 3

4 Passwords Cloud security cont. most common mechanism to authenticate users password guessing: physically looking, sniffing the net, social engineering, access database, outright guessing can be provided by security tokens (static password, synchronous dynamic password, asynchronous password, challenge response), memory cards and smart cards Biometrics modalities: fingerprints, retina, iris, voice, face, hand geometry, palm, handwritten signature dynamics requiements: accuracy, enrollment time (2 min), throughput (10/min) and accetability. Krutz and Vines, 2010 CWI-CTiF Workshop 2011, Copenhagen 4

5 Biometrics Biometric recognition is a process of recognizing the identity of a person based on intrinsic physiological or behavioral traits that the person possesses. Physiological characteristics are related to the shape of the body e.g. fingerprint. Behavioral are related to the behavior of a person, e.g. gait. A biometric recognition system can operate in two modes: verification and identification. Verification (or authentication) accepts or rejects the identity claim of a person. Identification determines which of the registered persons a given biometric data comes from. CWI-CTiF Workshop 2011, Copenhagen 5

6 Biometric recognition systems Physiological Biometrics Fingerprint fingertip patterns Facial recognition - facial characteristics Hand geometry - the shape of the hand Iris scan - features of colored ring of the eye Retinal scan - blood vessels in the eye Behavioral biometrics Speaker recognition - vocal behavior Signature/handwriting - signature dynamics CWI-CTiF Workshop 2011, Copenhagen 6

7 Speaker verification vs. identification Verification involves confirming/denying a persons claimed identity; Is this person n? ; one-to-one search. Claimed Identity Spkeaker n Accept Reject Identification is to establish a person's identity, i.e. to identify the speaker from a group of speakers; Who is this person? ; one-to-many search. Spkeaker 1 Identity Spkeaker N CWI-CTiF Workshop 2011, Copenhagen 7

8 Text-dependent vs. text-independent mode In text-independent recognition, the speaker can produce any speech. In text-dependent recognition, the speaker must pronounce a fixed or prompted phrase. Procedure Two parts: 1) enrolment and training, 2) testing. Three steps: 1) microphone recording, 2) preprocessing parameterization, 3) modeling and testing. CWI-CTiF Workshop 2011, Copenhagen 8

9 Speaker modeling Discriminative models are optimised to minimise the error on a set of training samples. multilayer perceptron support vector machines (SVMs) Generative models are probability density estimators that attempt to capture all of the underlying fluctuations and variations of the data. vector quantization Gaussian mixture models (GMMs) universal background model (UBM) hidden Markov models (HMMs) (text-dependent) Fusion, robustness, factor analysis, etc. CWI-CTiF Workshop 2011, Copenhagen 9

10 Evaluation: FAR and FRR False Acceptance Rate (FAR) and False Rejection Rate (FRR) FAR: the probability that an individual will be falsely accepted by the system FRR: the probability of the rejection of legitimate user Low FAR gives high security, yet results in high FRR making is less usable. DET (detection error trade-off) instead of ROC curve To compare the performance of the different biometric system the Crossover Error Rate (CER) can be used. The CER is defined as the error rate of the FAR=FRR. CWI-CTiF Workshop 2011, Copenhagen 10

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12 Speaker recognition: challenges NIST uses about 2.5 min of speech data. Methods for short training and test data (< 10 s) will be needed. Human-related error sources, such as the effects of emotions, vocal organ illness, aging, and level of attention. What if someone or some machine imitates me or just plays previously recorded signal back?. - a new horizon to study attack and defense in voice biometrics. In practical situations, mismatched mics for training and testing, limited training data, unbalanced text, noise and noncooperative users, real-time implementation, text/language dependency, channel effects, and cross-talk speech. Kinnunen & Li, An Overview of Text-Independent Speaker Recognition, Speech Communications, CWI-CTiF Workshop 2011, Copenhagen 12

13 SI for co-channel speech signals SSR levels from -9 to 6 db, 34 speakers 93.2%, 100% for 1st speaker. Saeidi, Mowlaee, Kinnunen, Tan, Christensen, Jensen, and Fränti, Improving Monaural Speaker Identification by Double-Talk Detection, Interspeech 2010, Japan. CWI-CTiF Workshop 2011, Copenhagen 13

14 Face recognition Three most significant streams of work: Principal Components Analysis (PCA) Linear Discriminant Analysis (LDA) Elastic Bunch Graph Matching (EBGM) CWI-CTiF Workshop 2011, Copenhagen 14

15 State-of-the-art for face recognition False rejection rate vs false acceptance rate. Better than human. Phillips, et al., FRVT 2006 and ICE 2006 Large-Scale Results, CWI-CTiF Workshop 2011, Copenhagen 15

16 Fusion of 2D and 3D for Face recognition Texas 3D Face Recognition Database: D and 3D facial image pairs of different subjects. In this experiment, 12 different subjects are used for training and 95 for testing (including the 12). Kinect. Fusion method covariance matrix project grey-scale and depth images separately and then compute two distances L R and L D, respectively Sahnoun, et al., Subspace based hybrid fusion of 2D and 3D modalities for face recognition, CWI-CTiF Workshop 2011, Copenhagen 16

17 Design of biometric systems Design of a biometric system needs to consider factors e.g. the available sensors the performance of various biometric recognition technology existing security infrastructure cost user acceptance. With the recent advance in biometric recognition techniques and low-cost sensors, we can expect the increasing deployment of biometric recognition in many fields including cloud communication. CWI-CTiF Workshop 2011, Copenhagen 17

18 Biometric authentication for the cloud A secure data access scheme based on identity-based encryption and biometric authentication for cloud communication Cheng, Rong, Tan, Zeng, "Identity based Encryption and Biometric Authentication Scheme for Secure Data Access in Cloud Computing," accepted by Chinese Journal of Electronics. CWI-CTiF Workshop 2011, Copenhagen 18

19 Biometric cloud communication The Department of Homeland Security (DHS) IDENT Database in 2010, hosts 110 million identities and enrolls or verifies over 125,000 individuals per day. A surge in enrolment, multimodality, tens of petabytes of biometric material. Design considerations Arbitrary records and metadata Common biometric matching API Real-time, parallel and bulk processing Flexibility, recovery, redundancy strategies Kohlwey, Sussman, Trost, Maurer, Leveraging the Cloud for Big Data Biometrics, 2011 IEEE World Congress on Services CWI-CTiF Workshop 2011, Copenhagen 19

20 Summary Cloud security Biometrics techniques and state-of-the-art Biometrics for secure data access in the cloud Biometics as an application in the cloud CWI-CTiF Workshop 2011, Copenhagen 20

21 Thanks for your attention! CWI-CTiF Workshop 2011, Copenhagen 21