Chaotic-Based Encryption Algorithm using Henon and Logistic Maps for Fingerprint Template Protection

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Apri Siswanto

Abstract

Fingerprint is a reliable user authentication method as it is unique to individual users that makes it efficient for authenticating users. In a fingerprint authentication system, user fingerprint information is stored in databases in an image format known as a fingerprint template. Although fingerprint is reliable, the templates stored in the database are exposed to security threats either during the data transmission process over the network or in storage. Therefore, there is a need to protect the fingerprint template, especially in unsecured networks to maintain data privacy and confidentiality. Many past studies proposed fingerprint template protection (FTP) using chaotic-based encryption algorithms that are more suitable to secure images than conventional encryption such as DES, AES, and RSA. The chaotic-based encryption algorithms have been improved a lot in terms of their robustness. However, the robustness of the algorithm caused a trade-off to encryption speed where it remains an issue in FTP.  Hence, this study aims to improve the limitations found in the existing chaotic-based encryption algorithms for FTP by improving its encryption speed using Henon and Logistic map. A series of simulations were conducted using MATLAB to evaluate the performance of the proposed chaotic-based encryption algorithm for FTP through different analyses covering key sensitivity, histogram, correlations, differential, information entropy, and encryption/decryption speed. The performance proposed encryption algorithm was promising which could be a starting point for detailed analysis and implementation in real application domains.

Article Details

How to Cite
Siswanto, A. (2022). Chaotic-Based Encryption Algorithm using Henon and Logistic Maps for Fingerprint Template Protection. International Journal of Communication Networks and Information Security (IJCNIS), 12(1). https://doi.org/10.17762/ijcnis.v12i1.4395 (Original work published April 26, 2020)
Section
Research Articles