SentinelTouch: A Lightweight Privacy-Preserving Biometric-Fingerprinting Authentication and Identification System Based on Neural Networks and Homomorphic Encryption
Authors: Nges Brian Njungle (STAM Center, Arizona State University), Eric Jahns (STAM Center, Arizona State University), Mishel Jyothis Paul (STAM Center, Arizona State University), Michel A Kinsy (STAM Center, Arizona State University)
Volume: 2026
Issue: 2
Pages: 143–156
DOI: https://doi.org/10.56553/popets-2026-0041
Abstract: Biometric fingerprint authentication and identification systems are increasingly deployed, yet widespread adoption in cloud and server-based platforms remains hindered by privacy and security concerns. Unlike passwords, compromised fingerprints are immutable, making their secure storage and computation paramount. Homomorphic Encryption (HE) offers strong privacy guarantees for fingerprint data processing by enabling computation directly on encrypted data. However, the high dimensionality of fingerprint images and the complexity of the neural networks needed for accurate recognition creates significant bottlenecks, which hinder the practical deployment of HE in this domain. We introduce SentinelTouch, an open-source framework for privacy-preserving fingerprint authentication and identification that delivers both efficiency and accuracy in HE environments. Our key insight is a twofold optimization: (1) a preprocessing pipeline that reduces fingerprint image dimensions to as low as 28x28 while preserving most of its discriminative details, and (2) the design of a lightweight, HE-friendly neural network that generalizes effectively on this compact data. We evaluate two deployment pipelines: (1) a full-privacy pipeline, where encrypted images are processed entirely under HE settings, achieving user identification in a one-to-many setting in just 16 seconds. (2) A hybrid pipeline, where only encrypted embeddings are processed under HE settings, achieving one-to-many user identification in 284 milliseconds. Our results show a 10x and 2.5x speedup over the current state-of-the-art results in both pipelines, respectively. Across the SOKOTO and PolyU datasets, SentinelTouch achieves Rank-1 accuracies within +-0.1% of the leading encrypted systems. This work demonstrates the practicality of end-to-end privacy-preserving fingerprint identification and authentication systems, offering HE security guarantees and utilizing neural networks, without compromising accuracy.
Keywords: Privacy-preserving, Biometric Fingerprinting System, Neural Networks, Homomorphic Encryption
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