Design of a Blockchain-Powered Biometric Template Security Framework Using Augmented Sharding

Design of a Blockchain-Powered Biometric Template Security Framework Using Augmented Sharding

Sarika Khandelwal (G. H. Raisoni College of Engineering, Nagpur, India), Shaleen Bhatnagar (Presidency University, India), Nirmal Mungale (G. H. Raisoni College of Engineering, Nagpur, India), and Ritesh Kumar Jain (Geetanjali Institute of Technical Studies, India)
Copyright: © 2022 |Pages: 22
DOI: 10.4018/978-1-6684-5072-7.ch004
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Biometric templates must be secured with traceability, immutability, and high-trust capabilities. A variety of system models are proposed by researchers, most of which either utilize blockchains or machine learning for improved security and quality of service (QoS). The augmented sharding model is designed using light weight incremental learning framework, which assists in shard formation and management. Performance evaluation of the proposed model indicates that it is able to achieve high accuracy attack mitigation, along with low block mining delay and high throughput. This performance is compared with various state-of-the-art methods and an improvement of 10% in terms of delay and 14% in terms of throughput is achieved. Further, an attack detection accuracy of 99.3% is obtained for sybil, masquerading, and man in the middle (MITM) attacks. This text further recommends improvement areas which can be further researched for enhancing security and QoS performance of the proposed model.
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Securing biometric signatures is a multidomain task, which involves cryptanalysis, privacy protection, traceability integration, signal processing, classification, etc. In order to design a high efficiency secure biometric signature processing system, a wide number of system capabilities are needed. These includes but are not limited to, data security, resilience against tampering, high speed traceability, low computational complexity, and reduced power requirements. An example of such a model is depicted in figure 1, wherein processes like template enrolment, identity claim, and exception handling can be observed. The model utilizes user input for capturing biometric information including, fingerprints, facial data, etc. and provides this information for quality assessment, and feature extraction. The extracted features are given to a template database for storage, which is connected to a system administrator for managerial purposes. The connection between system admin and template matching module is facilitated using an application programming interface (API) layer. This layer exposes the data to various 3rd parties, which might be prone to spying, spoofing, or tampering attacks. Thus, there is a need for a security layer to be integrated between the system and any incoming and outgoing connections from it. Thus, the user data capturing connection, system administrator connection, application connection, and any other connection(s) must be secured using a high-performance security layer, which provides high-speed data read/write capabilities.

In order to design such a security layer, various cryptographic, key-exchange, privacy preservation, and machine learning approaches are proposed by the researchers over the years. A survey of these approaches can be observed from the next section of this text, wherein various nuances, advantages, limitations, and future issues are discussed.

Figure 1.

An identity verification model using biometrics


Based on this discussion, it was observed during blockchain mining, each block needs to be scanned for evaluating unique and rule-based hash values. This requires substantial delay with increasing chain length, which reduces scalability of the system. To improve scalability, machine learning models are used, which aims at reducing redundant calculations during storage and retrieval, thereby increasing storage and retrieval speeds. Some of these models have limited integration capabilities with blockchain, due to their internal build structure. Other interfaceable models do not provide a significant performance improvement because large length blockchains require mandatory hash verification and rule validations. Thus, problem statement of this text is to maintain high security with good scalability under different network conditions. To achieve a solution for this problem statement, section 3 discusses design of the proposed blockchain powered biometric template security framework using augmented sharding (BLTSAS), and its performance evaluation. This evaluation also includes comparison with various state-of-the-art methods, which assists in estimating any performance gaps in the proposed model. Finally, this text concludes with some interesting observations about the proposed work, and recommends methods to further improve its performance.

Key Terms in this Chapter

Application Programming Interface (API): It simplify software development and innovation by enabling applications to exchange data and functionality easily and securely.

Radio Frequency Identification (RFID): It is a type of passive wireless technology that allows for tracking or matching of an item or individual.

Man In The Middle (MITM) Attack: A man in the middle (MITM) attack is a general term for when a perpetrator positions himself in a conversation between a user and an application.

Block: Block is a place in a blockchain where information is stored and encrypted.

Convolutional Neural Network (CNN): A convolutional neural network (CNN) is a type of artificial neural network used in image recognition and processing.

Proof-of-Stake (PoS): Is a cryptocurrency consensus mechanism for processing transactions and creating new blocks in a blockchain. A consensus mechanism is a method for validating entries into a distributed database and keeping the database secure.

Public Key Crytography (PKC): Public key cryptography involves a pair of keys known as a public key and a private key (a public key pair), which are associated with an entity that needs to authenticate its identity electronically or to sign or encrypt data.

Proof-of-Authority (PoA): Is an algorithm used with blockchains that delivers comparatively fast transactions through a consensus mechanism based on identity as a stake.

Secure Hashing Algorithm in 256-Bit Mode (SHA256): SHA-256 is a patented cryptographic hash function that outputs a value that is 256 bits long.

Internet of Things (IoT): Describes physical objects (or groups of such objects) with sensors, processing ability, software, and other technologies that connect and exchange data with other devices and systems over the Internet or other communications networks.

Jitter: Is the variation in the time between packets arriving, caused by network congestion, timing drift, or route changes.

Proof of Authority (PoA): Is an algorithm used with blockchains that delivers comparatively fast transactions through a consensus mechanism based on identity as a stake.

Quality of Service (QoS): It is the use of mechanisms or technologies that work on a network to control traffic and ensure the performance of critical applications with limited network capacity.

Threshold Cryptosystem: The basis for the field of threshold cryptography, is a cryptosystem that protects information by encrypting it and distributing it among a cluster of fault-tolerant computers.

League Championship Algorithm (LCA): Is a population-based algorithm framework for global optimization over a continuous search space.

Hash: Hash is a function that meets the encrypted demands needed to solve for a blockchain computation.

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