Biometric Template Protection based on Hill Cipher Algorithm with Two Invertible Keys

The security of stored templates has become an important issue in biometric authentication systems this because most of the biometric attacks target the biometric database beside the difficulty of issuing the templates again. Thus, to protect the biometric templates it must be encrypted before storing in database. In this paper we proposed an efficient encryption method based on two invertible and random keys to enhance and overcome the weakness of hill cipher algorithm the keys generated using upper triangular matrices with Pseudo-Random Number Generator (PRNG) using two large and random encryption keys. The proposed encryption method provides sufficient security and protection for the biometric templates from attacks, where the experimental results showed high efficiency comparing with the traditional Hill Cipher and existing methods

An efficient indexing and retrieval of iris biometrics data using hybrid transform and firefly based K-means algorithm title

The explosive increase in the number of biometric images saved in most databases has made image indexing mandatory. These processes could influence the speed of data access as well as support their retrieval. Hence, researchers are focusing on how to determine suitable image features to be used for clustering and index, with an efficient searching process. The existing methods are unable to extract sufficient number of the most important features of iris image for clustering and indexing processes. However, one of the weaknesses of clustering is the process of extracting the most important features. A combination of three transformation methods, namely, Discrete Cosine Transformation (DCT), Discrete Wavelet Transform (DWT), and Singular Value Decomposition (SVD) for analyzing the iris image and for extracting its local features have yet to be utilized for image clustering and indexing. Another problem related to clustering is when choosing the initial centroids for each cluster randomly. To overcome this disadvantage, the Fireflies Algorithm (FA) was used because it has the ability to perform global searches and has quick convergence rate to optimize the initial clustering centers of the K-means algorithm, using a kind of weighted Euclidean distance to reduce the defects made by noise data and other uncertainties. This thesis presents a new method to extract the most relevant features of iris biometric images for indexing the database within minimum time and search area. The enhanced method combines three transformation methods for analyzing the iris image and extracting its local features. It uses a weighted K-means clustering algorithm based on the improved FA to optimize the initial clustering centers of K-means algorithm, known as Weighted K-means clustering-Improved Firefly Algorithm (WKIFA). For searches and retrieval, an efficient parallel technique has been presented by dividing the group of features into two b-trees based on index keys. Searches within a group can be done using a half-searching algorithm to improve the response time for data retrieval. The system has been tested on publicly available databases. The experimental results showed that the indexing system has a considerably low penetration rate of 0.98%, 0.13%, and 0.12%, and lower bin miss rate of 0.3037%, 0.4226%, and 0.2019% compared to the existing iris databases of the Chinese Academy of Science - Institute of Automation (CASIA), University of Bath (BATH), and Database of Indian Institute of Technology Kanpur (IITK), respectively. Results of the improved WKIFA showed that it was more effective for the clustering stage of the system. It even outperformed the traditional K-mean, by reducing the penetration rates to 0.131%, 0.088%, and 0.108%, and improving the accuracy by reducing the bin miss rate to 0.2604%, 0.309%, and 0.1548% of the aforementioned databases, respectively. Analysis of time complexity of retrieval showed that the computational complexity was reduced to O (log n), which was better than the existing methods

Multibiometric systems and template security survey

Multimodal biometric systems are capable of utilizing, more than one physiological or behavioral characteristic for enrolment either in verification or identification mode, It is generally believed that several biometric sources usually compensate for the weaknesses of single biometric fusion techniques. The features that extracted from the biometric samples considered a critical part of biometric system which is called biometric template it is one of the most crucial issues in designing a secure system

A new biometric template protection based on secure data hiding approach

Biometrics is a technology that has been widely used in many official and commercial identification applications. The extracted features from the biometric sample is called biometric template which is used during a biometric authentication process. The security of templates is the critical part of biometric system and one of the most crucial issues in designing a secure system. The proposed approach focused on combining data hiding and biometrics to take advantage of the benefits of both fields and develop the hiding technique to find a secure solution for protecting biometric data. We deal with dental as a first biometric source and a user's speech as a second biometric source at same time it a reliable key from a user's speech for enhanced the security of the system. Two of the popular methods are combined DWT and DCT in the proposed security system (SDHA) for embedding and extraction the secret data in order to compensate the drawbacks of both of them and to make the hidden information much more secure against the attacks, Wavelet Transform which use Dyadic Filters to decompose cover image into 4-Levels (HH, HL, LH and LL) and Discrete Cosine Transforms to convert a signal of the selected coefficients (HH, HL and LH) into elementary frequency components. Simply the proposed hiding method are summarized by dividing the secret data into three sections according to the percentages that have been entered by a user then distribute these sections into the three chosen coefficient sets (HH,HL and LH) of the cover image which is an excellent secure locations for data hiding. The results show the efficiency of the proposed method comparing with other method that used skin tone region of images, DWT method and simple LSB method

Efficient classifying and indexing for large iris database based on enhanced clustering method

Explosive growth in the volume of stored biometric data has resulted in classification and indexing becoming important operations in image database systems. A new method is presented in this paper to extract the most relevant features of iris biometric images for indexing the iris database. Three transformation methods DCT, DWT and SVD were used to analyse the iris image and to extract its local features. The clustering method shouldering on the responsibility of determining the partitioning and classification efficiencies of the system has been improved. In the current work, the new Weighted K-means algorithm based on the Improved Firefly Algorithm (WKIFA) has been used to overcome the shortcomings in using the Fireflies Algorithm (FA). The proposed method can be used to perform global search and exhibits quick convergence rate while optimizing the initial clustering centers of the K-means algorithm. From the experimental results, the proposed method was indeed more effective for clustering and classification and outperformed the traditional k-mean algorithm. The Penetration Rates underwent reductions and reached the levels of 0.98, 0.13 and 0.12 for three different databases. Also, the Bin Miss Rates decreased to 0.3037, 0.4226 and 0.2019 for the investigated databases

Iris template protection based on enhanced hill cipher

Biometric is uses to identify authorized person based on specific physiological or behavioral features. Template protection is a crucial requirement when designing an authentication system, where the template could be modified by attacker. Hill Cipher is a block cipher and symmetric key algorithm it has several advantages such as simplicity, high speed and high throughput can be used to protect Biometric Template. Unfortunately, Hill Cipher has some disadvantages such as takes smaller sizes of blocks, very simple and vulnerable for exhaustive key search attack and known plain text attack, also the key matrix which entered should be invertible. This paper proposed an enhancement to overcome these drawbacks of Hill Cipher by using a large and random key with large data block, beside overcome the Invertible-key Matrix problem. The efficiency of encryption has been checked out by Normalized Correlation Coefficient (NCC) and running time

Anti-forensic steganography method based on randomization

Information hiding is a technique that embeds secret information into digital contents such as images, audios, movies, documents, etc. This work presents an anti-forensic steganography method that can embed and extract messages from images, which uses the same principle of LSB. The proposed model combines cryptography and steganography. First, the secret information are encrypted using Rijndael Encryption Algorithm. Then, the cover image is divided into several matrices. The number of matrices will be determining by a user, by entering a number, which will also be used to generate a set of random numbers. However, these random numbers will be the index to hiding the encrypted data bits randomly in the least significant bits of pixel channels. This randomization is expected to increase the security of the system as well as the capacity. The metric used for image quality are Peak Signal to Noise Ratio (PSNR) and Correlation Coefficient (Corr). Experimental results show that the proposed method can provide high data security and capacity

A survey of multi-biometrics and fusion levels

The verification of the identities of individuals is becoming an increasingly important requirement in a variety of applications based on specific physiological or behavioral features. Most biometric systems that are currently in operation usually utilize a single biometric trait which called Uni-biometric systems. Other systems are called Mutli-biometrics systems which are utilize, or are capable of utilizing, more than one physiological or behavioral characteristic for enrolment either in verification or identification mode. It is generally believed that by integrating various biometric traits into one single unit, the limitations of uni-biomatic systems can be alleviated. Given that several biometric sources usually compensate for the weaknesses of single biometric fusion techniques has dealt primarily with the fusion at the score matching level

A new secure storing system for biometric templates based encryption and concealment

The security of templates is the critical part of biometric system and one of the most crucial issues in any proposed system. In fact, features such as voice, face, fingerprints and many others can be covertly acquired or stolen by an attacker and misused. Therefore, storing biometric templates a secure way is crucial. This study proposes a novel approach that combines an improved encryption method with a new concealment technique to establish a secure data template storing system. The Hill Cipher algorithm has been improved to be more secure by using a large and random key with large data block and also extending it to include the special characters. In the other hand a new concealment method proposed by combine two of the popular methods DWT and DCT for embedding and extraction the secret data in order to compensate the drawbacks of both of them and to make the hidden information much more secure against the attacks, wavelet transform which use dyadic filters to decompose cover image into 4-levels (HH, HL, LH and LL) and discrete cosine Transforms to convert a signal of the selected coefficients (HH, HL and LH) into elementary frequency components then according to the percentages entering by a user the encrypted data will distribute. The efficiency of encryption and concealment have been checked out with a number of widely used metrics such as Peak Signal to Noise Ratio (PSNR) and Normalized Correlation Coefficient (NCC)

A novel local network intrusion detection system based on support vector machine

Problem statement: Past few years have witnessed a growing recognition of intelligent techniques for the construction of efficient and reliable Intrusion Detection Systems (IDS). Many methods and techniques were used for modeling the IDS, but some of them contribute little or not to resolve it. Approach: Intrusion detection system for local area network by using Support Vector Machines (SVM) was proposed. First, the intrusion ways and intrusion connecting of Local Area Network were defined for putting forward the design requests on intrusion detection system of LAN. Second, the new method to recognized attack patterns which may give better coverage and make the detection more effective. Results and Conclusion: SVM was used as a detection system that recognizes anomalies and raises an alarm. The data that was used in our experiments originated from a campus lab. The result of the evaluation produced a better result in terms of the detection efficiency and false alarm rate