Blind Authentication: A Secure Crypto-BiometricVerification Protocol

Rohan Abraham223R7

Guided byProf L

In a Nutshell

•A protocol for blind biometric authentication

•Blind - does not reveal biometric information to the authenticating server or vice versa

•Secure – encrypted biometrics, template protection

CONTENTS

•Biometric Authentication •Blind Biometric Authentication Protocol

using a linear classifier•Security & Privacy Issues•Implementation using SVM•Analysis – Accuracy and Biometric

Verification•Future Possibilities

Biometrics

• Sensor-based recognition of identity using biological signatures like face, fingerprints, hand geometry and iris which are unique to an individual

• Primary concerns 1. Template Protection – biometric of a person does not

change and cannot be replaced once compromised.

2. User’s privacy need to be preserved

3. Trust between user & Server

Server may not be trustworthy or competent to handle user’s plain biometric while the server has to find out if the user is genuine.

4. Network Security (third party intruders)

What is Blind Authentication ?

A biometric authentication protocol that does not reveal any:

▫information about the biometric samples to the authenticating server.

▫information regarding the classifier, employed by the server, to the user or client

How it works

Affiliated terms

•x- Feature vector of the sample (length n)•ω- Feature vector of the template(length

n)•τ- Threshold value•E(x)- encryption function•λ,r- random numbers generated as a part

of the encryption scheme•n- Number of features recognized by the

sensor and is static for a given biometric.

Homomorphic Encryption • An encryption scheme using which some

algebraic operation , like addition or multiplication, can be directly done on the cipher text

Identity Validation Process

Algorithm: Authentication

BLIND SECURE PRODUCT PROTOCOL

Receive from client

Server computes kn+k random numbers such that

Server computes

and send it to the client.Client decrypts it.

Client computes

to the server

Server computes

Send

Algorithm analysis

•The server carries out all its computation in the encrypted domain and hence does not get any information about x or ω.

•Privacy is based on the server’s ability to generate random numbers. The server has access to a random number generator(PRNG)

•Encrypted information is unable to be deciphered and the final SOP expression is obtained which is congruent with the original weighted product

•The following condition is the reason why the server is able to compute S in step(8).

Enrollment Phase

Enrollment based on a trusted third party(TTP).At the time of registering with the website, the encrypted version of the user’s biometric template is made available to the website.

Algorithm: Enrollment

System Security

• Biometric systems are more secure when compared to passwords or tokens as they are difficult to reproduce.

• The security is further enhanced by the fact that the attacker needs to get access to both the user’s biometric as well as the private key to be able to pose as an enrolled user.

• Security breaches can occur at the server, client or the network.

Server Security

•Case 1: Hacker gains access to the template database.

•Case 2: Hacker is in the database server during authentication.

•Case 3:Impostor trying blind attacks from a remote machine.

Client Security•Case 4:

Hacker gains access to the user’s biometric or private key.

•Case 5:Passive attack at the user’s computer.

Network security

•Attacker gains access to the network•The confidentiality of the data flow over

the network can be ensured using standard cryptographic methods like ciphers and digital signatures.

•All traffic is encrypted either using client’s public key or random numbers generated by server.

Privacy

•Concern of revealing personal information: Since original template or test sample is not revealed to the server, only identity is established.

•Concern of being tracked: Different keys for different applications(servers), thereby avoid being tracked across uses.

• An SVM (Support Vector Machine) classifier based on client- server architecture for the evaluation of the protocol was implemented in GNU/C• SVM is a supervised learning method which can be used for classification. Given a set of training examples, each marked as belonging to one of two categories, an SVM training algorithm builds a model that assigns new examples into one category or the other.•An SVM model is a representation of the examples as points in space  mapped so that the examples of the separate categories are divided by a clear gap

Implementation and Analysis

• RSA and Paillier cryptosystems are used to generate keys, both of which are public key encryption schemes. They are public-key cryptosystems and are widely used for secure data transmission. The encryption key is public and differs from the decryption key which is kept secret.

•The feature vectors of the SVM are scaled and rounded off to integers. An implicit sign representation is used to handle negative numbers. If the range is (0,M) we use (0,M/2) for positive numbers and the remaining numbers for negative. Let M=256,then -95 is represented as -95modulo256=-95+256=161

•A parameter is encrypted by mapping the integral number to implicit sign representation and reverse mapping is done by the server on the results.

•

IMPLEMENTATION AND ANALYSIS•Experiments designed to evaluate the efficiency and accuracy of proposed approach.•For evaluation, an SVM based verifier based on client- server architecture was implemented.

Verification time for various key sizes and feature vector lengths

ROC CURVES FOR VERIFICATION

Advantages of Blind Authentication• Fast and Provably Secure authentication

without trading off accuracy.• Supports generic classifiers such as Neural

Network and SVMs.• Useful with wide variety of fixed-length

biometric- traits.• Ideal for applications such as biometric

ATMs, login from public terminals.

Disadvantages

• Foolproof Enrollment procedures are necessary to ensure that the biometric is secure from any type of attacks (e.g. a malicious enrollment server)

• Success of Biometric authentication is solely dependent on the invariance of physical features. Therefore, if the biometric is damaged by injury (hand geometry) or disease (cataract for iris recognition) or presence of foreign substances (like water or oil), then the encryption algorithm may not give expected results.

Conclusions• Verification can be done in real-time with the

help of available hardware

• Keep the interaction between the user and the

server to a minimum

• Extensions to this work includes secure

enrollment protocols and encryption

methods to reduce computations

• Dynamic warping based matching of variable

length feature vectors can further enhance the

utility of the approach

References

•N. K. Ratha, J. H. Connell, and R. M. Bolle, “Enhancing security and privacy in biometrics-based authentication systems”

•Maneesh Upmanyu, Anoop M. Namboodiri, K. Srinathan and C.V. Jawahar, “Blind authentication: A secure crypto-biometric verification protocol”

IEEE-Transactions on Information Forensics and Security (IEEE-TIFS, June 2010)