Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Benny, Sandra; Das, Deepthi; Rajesh Kanna, R. Title A name given to the resource Security and Privacy in Biometric Authentication: Advancements and Risks Across Platforms Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Proceedings - 2025 International Conference on Transformative Computing Technologies, ICTCT 2025;pp.254-259 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICTCT69201.2025.00056" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICTCT69201.2025.00056</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105037440350?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105037440350?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Benny S., Christ University Computer Science, Bengaluru, India; Das D., Christ University Statistics and Data Science, Bengaluru, India; Rajesh Kanna R., Christ University Computer Science, Bengaluru, India Description An account of the resource Biometric identification systems like fingerprint, face, iris, and voice recognition have revolutionized user authentication with more convenience and increased security over conventional password based authentication. Nonetheless, the fact that biometric features are immutable introduces unprecedented issues around privacy, spoofing, and database compromise because once biometric data leaks out, it cannot be withdrawn or substituted. This work advances empirical testing with analytical analysis to test biometric authentication for face, fingerprint, and iris modalities. Models based on convolutional neural networks (CNNs) were used and evaluated on benchmarked datasets, and findings indicated that face and iris recognition performed nearly perfectly with zero false acceptance and rejection rates in controlled environments, while fingerprint recognition performed poorly because of dataset size and quality constraints. The conclusions identify the significance of data preparation and variation in ascertaining the reliability of biometric information. In an effort to cross-verify the experiments further, case studies of Aadhaar, Apple Face ID, and Biostar 2 also clarified the threats of central storage, spoofing, and regulatory loopholes. The research concludes by suggesting privacy preserving frameworks, encryption, and multimodal methods for securing the future of biometric authentication. 2025 IEEE. Subject The topic of the resource Biometric authentication; Face recognition; Fingerprint recognition; Iris recognition; Privacy and security Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833159195-3; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online