The widespread adoption of Machine Learning as a Service raises critical privacy and security concerns, particularly about data confidentiality and trust in both cloud providers and the machine learning models provided. Homomorphic Encryption (HE) has emerged as a promising solution to these problems, allowing computations on encrypted data without decryption. Despite its potential, existing works that integrate HE into neural networks are often limited to specific architectures or classes. This leaves a wide gap in providing a framework for easy development of HE-friendly privacy-preserving neural network models similar to what we have in the broader field of machine learning. In this paper, we present FHEON, an open-source configurable framework for developing privacy-preserving neural network models for inference using the CKKS scheme of HE. FHEON introduces optimized and configurable implementations of privacy-preserving neural network layers, including convolution layers, average pooling layers, ReLU activation functions, and fully connected layers. These layers are configured using standard parameters such as input channels, output channels, kernel size, stride, and padding to support arbitrary convolution neural network (CNN) architectures. Furthermore, FHEON provides utility functions that ease usage and adoption. We assess the performance of FHEON using several CNN architectures, including LeNet-5, VGG-11, VGG-16, ResNet-20, and ResNet-34. FHEON maintains encrypted-domain accuracies within +-1% of their plaintext counterparts for ResNet-20 and LeNet-5 models. Notably, on a consumer-grade CPU, the models built on FHEON achieved 98.5% accuracy with a latency of 13 seconds on MNIST using LeNet-5, and 92.2% accuracy with a latency of 403 seconds on CIFAR-10 using ResNet-20. Though configurable, FHEON outperform all state-of the-art HE inference works in both latency and memory utilization. Additionally, FHEON operates within a practical memory budget requiring not more than 42.3 GB for VGG-16.

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.

Collision-resistant cryptographic hash functions (CRHs) are crucial for security, particularly for message authentication in Zero-knowledge Proof (ZKP) applications. However, traditional CRHs like SHA-2 or SHA-3, while optimized for CPUs, generate large circuits, rendering them inefficient in the ZK domain. Conversely, ZK-friendly hashes are designed for circuit efficiency but struggle on conventional hardware, often orders of magnitude slower than standard hashes due to their reliance on expensive finite field arithmetic. To bridge this performance gap, we present HashEmAll, a novel collection of FPGA-based realizations for three prominent ZK-friendly hashes: Griffin, Rescue-Prime, and Reinforced Concrete. Each offers distinct optimization pro les, with both area-optimized and latency-optimized variants available, allowing users to tailor hardware selection to specific application constraints regarding resource utilization and performance.Our extensive evaluation shows that latency-optimized HashEmAll designs outperform CPU implementations by at least 10×, with the leading design achieving a 23× speedup. These gains are coupled with lower power consumption and compatibility with accessible FPGAs. Importantly, the highly parallel and pipelined architecture of HashEmAll enables significantly better practical scaling than CPU-based approaches towards building real-world ZKP applications, such as data commitments with Merkle Trees, by mitigating the hashing bottleneck for large trees. This highlights the suitability of HashEmAll for real-world ZKP applications involving large-scale data authentication. We also highlight the ability to translate the HashEmAll methodology to various ZK-friendly hash functions and different field sizes.

Collision-resistant, cryptographic hash (CRH) functions have long been an integral part of providing security and privacy in modern systems. Certain constructions of zero-knowledge proof (ZKP) protocols aim to utilize CRH functions to perform cryptographic hashing. Standard CRH functions, such as SHA2, are inefficient when employed in the ZKP domain, thus calling for ZK-friendly hashes, which are CRH functions built with ZKP efficiency in mind. The most mature ZK-friendly hash, MiMC, presents a block cipher and hash function with a simple algebraic structure that is well-suited, due to its achieved security and low complexity, for ZKP applications. Although ZK-friendly hashes have improved the performance of ZKP generation in software, the underlying computation of ZKPs, including CRH functions, must be optimized on hardware to enable practical applications. The challenge we address in this work is determining how to efficiently incorporate ZK-friendly hash functions, such as MiMC, into hardware accelerators, thus enabling more practical applications. In this work, we introduce AMAZE, a highly hardware-optimized open-source framework for computing the MiMC block cipher and hash function. Our solution has been primarily directed at resource-constrained edge devices; consequently, we provide several implementations of MiMC with varying power, resource, and latency profiles. Our extensive evaluations show that the AMAZE-powered implementation of MiMC outperforms standard CPU implementations by more than 13×. In all settings, AMAZE enables efficient ZK-friendly hashing on resource-constrained devices. Finally, we highlight AMAZE's underlying open-source arithmetic backend as part of our end-to-end design, thus allowing developers to utilize the AMAZE framework for custom ZKP applications.