DistanceBased Encryption: How to Embed Fuzziness in BiometricBased Encryption
DistanceBased Encryption: How to Embed Fuzziness in BiometricBased Encryption
ABSTRACT:
We introduce a new encryption notion called distancebased encryption (DBE) to apply biometrics in identity based encryption. In this notion, a ciphertext encrypted with a vector and a threshold value can be decrypted with a private key of another vector, if and only if the distance between these two vectors is less than or equal to the threshold value. The adopted distance measurement is called Mahalanobis distance, which is a generalization of Euclidean distance. This novel distance is a useful recognition approach in the pattern recognition and image processing community. The primary application of this new encryption notion is to incorporate biometric identities, such as face, as the public identity in an identitybased encryption. In such an application, usually the input biometric identity associated with a private key will not be exactly the same as the input biometric identity in the encryption phase, even though they are from the same user. The introduced DBE addresses this problem well as the decryption condition does not require identities to be identical but having small distance. The closest encryption notion to DBE is the fuzzy identitybased encryption, but it measures biometric identities using a different distance called an overlap distance (a variant of Hamming distance) that is not widely accepted by the pattern recognition community, due to its long binary representations. In this paper, we study this new encryption notion and its constructions. We show how to generically and efficiently construct such a DBE from an inner product encryption (IPE) with reasonable size of private keys and ciphertexts. We also propose a new IPE scheme with the shortest private key to build DBE, namely, the need for a short private key. Finally, we study the encryption efficiency of DBE by splitting our IPE encryption algorithm into offline and online algorithms.
PROJECT OUTPUT VIDEO:
EXISTING SYSTEM:

In the cryptography research community, the use of biometric identity in identitybased encryption was first mentioned by Sahai and Waters. They formalized the notion called Fuzzy IBE, which allows for errortolerance properties of a private key of a biometric identity to decrypt a ciphertext encrypted with a slightly different biometric identity.

The fuzzy IBE judges the similarity of the two biometric identities using a set overlap distance, which is a variant of Hamming distance. We note that this distance requires the biometric identities to be represented with binary strings.
DISADVANTAGES OF EXISTING SYSTEM:

The corresponding pattern recognition technique must process biometric identities into long enough binary strings for highprecision recognition.

Because of this, in comparison with the Euclidean distance, the Hamming distance is not such a widely accepted approach in the pattern recognition and image processing community.
PROPOSED SYSTEM:
In this paper, we define distance based encryption and study its constructions under Mahalanobis distance. We show how to compactly transform the Mahalanobis distance between vectors and into an inner product between evolved vectors Then, we give a generic construction of DBE from a novel encryption notion called innerproduct encryption (IPE). The technical contributions to this work are as follows.

Firstly, we propose a generic construction of DBE from IPE with reasonable sized private keys and ciphertexts. Given any integer defined by the system generator, each DBE key has numbers of IPE keys and each DBE ciphertext has numbers of IPE ciphertext. We can choose a proper to balance the size between the private key and ciphertext.

Secondly, we propose a new IPE scheme with the shortest private key. The private key in our proposed scheme comprises of two group elements only, compared to nine group elements in the literature. The DBE implementation from our IPE will therefore save more than 75% of secure memory for private key storage. Our IPE scheme is selectively secure with the payload security under the Decision Bilinear DiffieHellman assumption.

Finally, we show how to split the encryption of the proposed IPE scheme into offline and online phases. The offline/online separation only costs modular multiplications in the online encryption phase and adds one more point multiplication in the corresponding decryption. It can be applied to DBE to speed up the encryption computation.
ADVANTAGES OF PROPOSED SYSTEM:

Our proposed IPE scheme into online/offline IPE, which is highly useful in improving the encryption efficiency of DBE.

We proposed a new IPE whose private key is the shortest composed of two group elements.
MODULES:
 Private Key Generator
 Encryptor
 Decryptor
MODULE DESCRIPTION:
The distance based encryption (DBE) is composed of three entities: private key generator (PKG), encryptor and decryptor.
1. Private Key Generator:

The private key generator is the trusted third party who computes private keys of biometrics for users (i.e. decryptors). The PKG needs to verify that the registered biometric belongs to the user before computing its private key for the user.

The PKG takes as input the registered biometric and a master secret key. It runs the recognition algorithm to obtain a vector of this biometric and then runs the key generation of DBE to compute a private key of this vector for the user.
2. Encryptor:

The encryptor can be any entity who wants to send a sensitive message to a decryptor, where the message is encrypted with one of the receiver’s biometrics. The recognition algorithm is first called to extract vector of this biometric. Then, the encryption algorithm of DBE is called to encrypt the message using and a threshold value t.

The encryptor can set t = tu in the cipher text generation, which means the encryptor wants the decryptor to have a private key on a vectorclose to under the standard recognition. Notice that if t < tu, this means the encryptor wishes that be much closer tothan the standard recognition.
3. Decryptor:

Given a cipher text created with(,t) and a private key of , the decryptor shall decrypt the message if the distance between and is less than or equal to t.

The decryptor conducts the decryption by running the decryption algorithm of DBE. There is no need for processing biometrics for the decryptor as both biometrics have been transformed into vectors.
SYSTEM REQUIREMENTS:
HARDWARE REQUIREMENTS:

System : Pentium Dual Core.

Hard Disk : 120 GB.

Monitor : 15’’ LED

Input Devices : Keyboard, Mouse

Ram : 1GB.
SOFTWARE REQUIREMENTS:

Operating system : Windows 7.

Coding Language : MATLAB

Tool : MATLAB R2013A
REFERENCE:
Fuchun Guo, Willy Susilo, Senior Member, IEEE, and Yi Mu, Senior Member, IEEE, “DistanceBased Encryption: How to Embed Fuzziness in BiometricBased Encryption”, IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, VOL. 11, NO. 2, FEBRUARY 2016