In a world where security and efficiency are paramount, the need for robust biometric authentication is ever-growing. Traditional authentication methods have become increasingly unreliable due to the constant threat of cyber-attacks. Biometric authentication, especially fingerprint recognition, has emerged as a promising alternative. However, it brings concerns related to data storage and centralized repositories, which can leave it vulnerable to security breaches. In this editorial, we introduce a groundbreaking solution – a blockchain-based fingerprint authentication system that leverages zero-knowledge proofs, specifically (Zero-Knowledge Succinct Non-interactive Argument of Knowledge). This innovative approach ensures secure and efficient authentication while safeguarding sensitive biometric information. zk-SNARKs The State of Biometric Identity Management Biometric authentication utilizes an individual's unique biological characteristics, such as fingerprints, iris patterns, or facial features, to verify their identity. Among these, fingerprint authentication stands out for its ease of use, accuracy, and non-intrusive nature. Its applications are diverse, ranging from securing facilities to mobile devices and financial transactions. However, traditional fingerprint-based identity management systems face significant challenges: Centralized Storage: Storing biometric data in a central database exposes it to potential breaches. Privacy Concerns: Biometric data is highly sensitive personal information, raising privacy concerns. Data Misuse: The risk of data misuse or unauthorized access is a real threat. Lack of Transparency: Proprietary algorithms for fingerprint matching lack transparency and scrutiny. Blockchain Integration for Enhanced Identity Management Blockchain technology, known for its immutable and secure ledger capabilities, has gained prominence across industries. In the realm of identity management, it empowers individuals to control their personal information while ensuring the utmost security and privacy. The integration of blockchain with biometric identity management not only enhances security but also revolutionizes identity verification in the digital age. To tackle the security and privacy issues in identity management, we introduce Zero-Knowledge Succinct Non-interactive Argument of Knowledge (zk-SNARK). These allow users to prove knowledge of a secret without revealing the secret itself. zk-SNARKs are instrumental in minimizing data storage requirements on the blockchain, as the proofs themselves are compact, enhancing blockchain scalability and reducing transaction times. zero-knowledge proofs The Potential of zk-SNARK in Blockchain-Based Identity Management As blockchain technology continues to evolve, the incorporation of zk-SNARKs holds significant promise for addressing scalability challenges and enhancing data privacy and security in blockchain-based identity management solutions. This groundbreaking approach ensures the robustness of identity verification processes in various domains, including financial services, healthcare, and more. Motivation and Contribution This article introduces zk-SNARKs to a blockchain-based identity management system and employs a K-Nearest Neighbors (KNN) based approach to generate cancelable templates for fingerprint authentication, which are stored using the InterPlanetary File System (IPFS). The key contributions of our work are as follows: We implement an efficient KNN-based algorithm for generating cancelable templates. Efficient KNN-S-Based Algorithm: enhances data security and privacy in biometric data storage. Enhanced Data Security: Incorporation of zero-knowledge proofs We integrate the InterPlanetary File System (IPFS) for decentralized and distributed storage of templates. Decentralized Storage: Proposed Approach Our proposed approach focuses on developing a blockchain-based identity management system that utilizes zk-SNARK for authentication and employs a KNN-based approach for fingerprint template generation. The system includes the following key components: Enhancements are applied to fingerprint images to improve contrast, brightness, and detail. This includes preprocessing, enhancement, and postprocessing steps. Fingerprint Image Enhancement: Minutiae features, such as ridge endings and bifurcations, are extracted from fingerprint images for recognition and comparison. Minutiae Points Extraction: A KNN structure is created for each minutiae point, facilitating user registration and authentication. Generating K-Nearest Neighborhood Structure: The KNN structure is quantized into a 2D array, making it ready for further processing. Quantization of K-Nearest Neighborhood Structure: The 2D array is transformed into a 1D bit string, simplifying data representation. Converting the 2D Array into a 1D Bit String: The bit string is further transformed, ensuring the template's security and integrity. Transforming 1D Bit String to Final Template: The stored fingerprint template and query fingerprint template are compared to generate a matching score, which indicates the level of similarity between the two. Matching for Authentication: The final cancelable KNN template is stored on the InterPlanetary File System (IPFS), providing a decentralized and secure storage solution. Storage of Template using Interplanetary File System: Zero-Knowledge Succinct Non-interactive Argument of Knowledge (zk-SNARK) To ensure the validity of identity claims without revealing sensitive information, zk-SNARKs play a crucial role in our system. These succinct proofs allow users to demonstrate knowledge of a secret without disclosing the secret itself. The zk-SNARK proof generation and verification process involves several steps: The computational statement is translated into an arithmetic circuit, representing the system's constraints. Generating Arithmetic Circuit: The arithmetic circuit is converted into a Rank-1 Constraint System (R1CS) to express constraints on variables through linear equations. Converting the Circuit to R1CS: A multi-party computation protocol is used to create public parameters for generating and verifying the proof, ensuring Generating a Trusted Setup: trustworthiness. The user generates a zk-SNARK proof of knowledge for the system, which is verified by the verifier, without revealing the secret information. Proving and Verification: zk-SNARKs provide a secure and scalable solution for blockchain-based identity management by minimizing the data stored on the blockchain while maintaining the highest level of security and privacy. Conclusion and zk-SNARKs are shaping the future of identity management by offering enhanced security and privacy for biometric authentication. The proposed system combines the strengths of blockchain, zk-SNARKs, and KNN-based fingerprint template generation to create a secure, efficient, and decentralized identity management solution. As blockchain technology continues to mature, integrating zk-SNARKs in biometric authentication systems offers a promising avenue for addressing data security and privacy concerns in an increasingly interconnected world. Blockchain technology By leveraging zk-SNARKs in blockchain-based identity management, individuals can confidently assert their identities while keeping their biometric information safe, empowering a new era of secure digital interactions. This innovative approach paves the way for a future where privacy, security, and efficiency coexist in perfect harmony. References Dwivedi, R., Dey, S. (2017). Coprime mapping transformation for protected and revocable fingerprint template generation. International Conference on Mining Intelligence and Knowledge Exploration. Blum, M., De Santis, A., Micali, S., Persiano, G. (1991). Noninteractive zero-knowledge. SIAM Journal on Computing, 20(6), 1084–1118. Guo, C., You, L., Hu, G. (2022). A novel biometric identification scheme based on zero-knowledge succinct noninteractive argument of knowledge. Security and Communication Networks, 2022, 1–13. doi:10.1155/2022/2791058. Luong, D. A., Park, J. H. (2023). Privacy-preserving identity management system on blockchain using zk-snark. IEEE Access, 11, 1840–1853. doi:10.1109/ACCESS.2022.3233828. ElGayyar, M., El Yamany, H., Grolinger, K., Capretz, M., Mir, S. (2020). Blockchain-based federated identity and auditing. International Journal of Blockchains and Cryptocurrencies, 1, 179. doi:10.1504/IJBC.2020.109004. Xu, J., Xue, K., Tian, H., Hong, J., Wei, D. S. L., Hong, P. (2020). An identity management and authentication scheme based on redactable blockchain for mobile networks. IEEE Transactions on Vehicular Technology, 69(6), 6688–6698. doi:10.1109/TVT.2020.2986041. Alsayed Kassem, J., Sayeed, S., Marco-Gisbert, H., Pervez, Z., Dahal, K. (2019). Dns-idm: A blockchain identity management system to secure personal data sharing in a network. Applied Sciences, 9(15). doi:10.3390/app9152953. Mao, X., Chen, Y., Deng, C., Zhou, X. (2023). A novel privacy-preserving biometric authentication scheme. PLOS ONE, 18(5). doi:10.1371/journal.pone.0286215. Effland, T., Schneggenburger, M., Schuler, J., Zhang, B., Hartloff, J., Dobler, J., Tulyakov, S., Rudra, A., Govindaraju, V. (2014). Secure fingerprint hashes using subsets of local structures. Sandhya, M., Prasad, M. (2015). k-nearest neighborhood structure (k-nns) based alignment-free method for fingerprint template protection.

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