SecureID-HE: Homomorphic Encryption for Privacy-Preserving Digital Identity Verification

Authors

  • Suman Kumar Sanjeev Prasanna, Lauren VanTalia

Keywords:

Privacy-Preserving, Digital Identity, Homomorphic Encryption, Encrypted Verification, Multi-Modal Authentication, Secure Computation, Biometric Security.

Abstract

Preserving privacy in digital identity verification is increasingly critical as multi-institution systemsexchange sensitive biometric and behavioral data. Traditional verification approaches typically requiredecryption during computation, exposing raw templates to potential breaches and undermining trust in large-scale deployments. This research introduces SecureID-HE, a homomorphic encryption

References

Anand, D. and V. Khemchandani, “Identity and access management systems,” in Security and Privacy of Electronic Healthcare Records: Concepts, Paradigms and Solutions, 2019, p. 61.

S. K. S. Prasanna, “GeoDNN: Geometry-aware deep neural networks for cross-domain fingerprint spoof detection,” Int. J. Intell. Syst. Appl. Eng., vol. 6, no. 1, pp. 97–107, Mar. 2018.

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Published

2024-04-20

How to Cite

Suman Kumar Sanjeev Prasanna, Lauren VanTalia. (2024). SecureID-HE: Homomorphic Encryption for Privacy-Preserving Digital Identity Verification . Journal of Computational Analysis and Applications (JoCAAA), 33(4), 1121–1132. Retrieved from https://eudoxuspress.com/index.php/pub/article/view/5281