Pattern mining approaches used in sensor-based biometric recognition: a review

Sensing technologies place significant interest in the use of biometrics for the recognition and assessment of individuals. Pattern mining techniques have established a critical step in the progress of sensor-based biometric systems that are capable of perceiving, recognizing and computing sensor data, being a technology that searches for the high-level information about pattern recognition from low-level sensor readings in order to construct an artificial substitute for human recognition. The design of a successful sensor-based biometric recognition system needs to pay attention to the different issues involved in processing variable data being - acquisition of biometric data from a sensor, data pre-processing, feature extraction, recognition and/or classification, clustering and validation. A significant number of approaches from image processing, pattern identification and machine learning have been used to process sensor data. This paper aims to deliver a state-of-the-art summary and present strategies for utilizing the broadly utilized pattern mining methods in order to identify the challenges as well as future research directions of sensor-based biometric systems

Developing an Algorithm for Securing the Biometric Data Template in the Database

This research article published by the International Journal of Advanced Computer Science and Applications, Vol. 10, No. 10, 2019In the current technology advancement, biometric template provides a dependable solution to the problem of user verification in an identity control system. The template is saved in the database during the enrollment and compared with query information in the verification stage. Serious security and privacy concerns can arise, if raw, unprotected data template is saved in the database. An attacker can hack the template information in the database to gain illicit access. A novel approach of encryption-decryption algorithm utilizing a design pattern of Model View Template (MVT) is developed to secure the biometric data template. The model manages information logically, the view shows the visualization of the data, and the template addresses the data migration into pattern object. The established algorithm is based on the cryptographic module of the Fernet key instance. The Fernet keys are combined to generate a multiFernet key to produce two encrypted files (byte and text file). These files are incorporated with Twilio message and securely preserved in the database. In the event where an attacker tries to access the biometric data template in the database, the system alerts the user and stops the attacker from unauthorized access, and cross-verify the impersonator based on the validation of the ownership. Thus, helps inform the users and the authority of, how secure the individual biometric data template is, and provided a high level of the security pertaining the individual data privac

Development of secured algorithm to enhance the privacy and security template of biometric technology

A Thesis Submitted in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy in Mathematical and Computer Science and Engineering of the Nelson Mandela African Institution of Science and TechnologyThe security of information and personal privacy are the growing concerns in today’s human life worldwide. The storage of biometric data in the database has raised the prospect of compromising the database leading to grave risks and misuse of the person’s privacy such as growth in terrorism and identity fraud. When a person’s biometric data stored is revealed, their security and privacy are being compromised. This research described a detailed evaluation on several outbreaks and threats associated with the biometric technology. It analyzed the user’s fear and intimidations to the biometric technology alongside the protection steps for securing the biometric data template in the database. It is known that, when somebody’s biometric data template is compromised from the database that consequently might indicate proof of identity robbery of that person. Mixed method to compute and articulate the results as well as a new tactic of encryption-decryption algorithm with a design pattern of Model View Template (MVT) are used for securing the biometric data template in the database. The model managed information logically, the view indicated the visualization of the data, and the template directed the data migration into pattern object. Factors influencing fear of biometric technology such as an exposer of personal information, improper data transfer, and data misuse are found. Strong knowledge of the ideal technology like the private skills of the biometric technology, data secrecy and perceived helpfulness are established. The fears and attacks along the technology like a counterfeit of documents and brute-force attack are known. The designed algorithm based on the cryptographic module of the Fernet keys instance are utilized. The Fernet keys are combined to generate a multiFernet key, integrated with biometric data to produce two encrypted files (byte and text file). These files are incorporated with Twilio message and firmly stored in the database. The storage database has security measures that guard against an impostor’s attack. The database system can block the attacker from unauthorized access. Thus, significantly increased individual data privacy and integrity

Security and Cryptographic Challenges for Authentication Based on Biometrics Data

Authentication systems based on biometrics characteristics and data represents one of the most important trend in the evolution of the society, e.g., Smart City, Internet-of-Things (IoT), Cloud Computing, Big Data. In the near future, biometrics systems will be everywhere in the society, such as government, education, smart cities, banks etc. Due to its uniqueness, characteristic, biometrics systems will become more and more vulnerable, privacy being one of the most important challenges. The classic cryptographic primitives are not sufficient to assure a strong level of secureness for privacy. The current paper has several objectives. The main objective consists in creating a framework based on cryptographic modules which can be applied in systems with biometric authentication methods. The technologies used in creating the framework are: C#, Java, C++, Python, and Haskell. The wide range of technologies for developing the algorithms give the readers the possibility and not only, to choose the proper modules for their own research or business direction. The cryptographic modules contain algorithms based on machine learning and modern cryptographic algorithms: AES (Advanced Encryption System), SHA-256, RC4, RC5, RC6, MARS, BLOWFISH, TWOFISH, THREEFISH, RSA (Rivest-Shamir-Adleman), Elliptic Curve, and Diffie Hellman. As methods for implementing with success the cryptographic modules, we will propose a methodology which can be used as a how-to guide. The article will focus only on the first category, machine learning, and data clustering, algorithms with applicability in the cloud computing environment. For tests we have used a virtual machine (Virtual Box) with Apache Hadoop and a Biometric Analysis Tool. The weakness of the algorithms and methods implemented within the framework will be evaluated and presented in order for the reader to acknowledge the latest status of the security analysis and the vulnerabilities founded in the mentioned algorithms. Another important result of the authors consists in creating a scheme for biometric enrollment (in Results). The purpose of the scheme is to give a big overview on how to use it, step by step, in real life, and how to use the algorithms. In the end, as a conclusion, the current work paper gives a comprehensive background on the most important and challenging aspects on how to design and implement an authentication system based on biometrics characteristics