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.

Binary instrumentation tools are widely used to facilitate the development of hardware and software systems. Traditionally, these tools are designed around a fixed Instruction Set Architecture (ISA) specification. However there is a shift in the architectural community towards open ISAs, whose key feature is the ability to add custom ISA extensions. The lack of extensibility in traditional binary instrumentation tools limits their capacity to adapt to these evolving ISAs, thus hindering their ability to analyze and modify binaries built for open ISAs.