arXiv subjects

Nges Brian Njungle

Publications and source records attributed to Nges Brian Njungle.

8 recordsLinked to original sources

arXiv2026

DALC-CT: Dynamic Analysis of Low-Level Code Traces for Constant-Time Verification

Timing side-channel attacks exploit variations in program execution time to recover sensitive information. Cryptographic implementations are especially vulnerable to these attacks, since even small timing differences in operations such as modular exponentiation or key comparisons can be exploited to extract highly sensitive information, such as secret keys. To mitigate this threat, implementations of programs that handle sensitive information are often expected to adhere to constant-time principles, ensuring that execution behavior does not depend on secret inputs. However, validating the constant-time property of programs remains a major challenge in cryptography development. Formal method approaches to verify constant-time implementations rely on abstractions that often fail to capture real execution behavior, while timing-based measurement techniques are highly sensitive to noise from other programs and even hardware environments. In this work, we propose a novel approach for verifying constant-time programs based on dynamic analysis of low-level execution traces. Our method measures instruction sequences across multiple input values for any given binary and targeted function. Any variations in the instruction mix distribution for any given pair of traces indicate a deviation from the constant-time principle and behavior. We developed an open-source tool called DALC-CT, for the constant-time verification of programs using this approach. We evaluated it on a set of well-known constant-time and non-constant-time examples, achieving a perfect detection of issues. Our results demonstrate that analyzing the logical execution of programs via instruction trace comparisons provides a lightweight and reliable way to verify the constant-time property of programs.

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arXiv2026

Towards Deep Encrypted Training: Low-Latency, Memory-Efficient, and High-Throughput Inference for Privacy-Preserving Neural Networks

Privacy-preserving machine learning (PPML) has become increasingly important in applications where sensitive data must remain confidential. Homomorphic Encryption (HE) enables computation directly on encrypted data, allowing neural network inference without revealing raw inputs. While prior works have largely focused on inference over a single encrypted image, batch processing of encrypted inputs lags behind, despite being critical for high-throughput inference scenarios and training-oriented workloads. In this work, we address this gap by developing optimized algorithms for batched HE-friendly neural networks. We also introduced a pipeline architecture designed to maximize resource efficiency for different batch size execution. We implemented these algorithms and evaluated our work using HE-friendly ResNet-20 and ResNet-34 models on encrypted CIFAR-10 and CIFAR-100 datasets, respectively. For ResNet-20, our approach achieves an amortized inference time of 8.86 seconds per image when processing a batch of 512 encrypted images, with a peak memory usage of 98.96 GB. These results represent a 1.78x runtime improvement and a 3.74x reduction in memory usage compared to the state-of-the-art design. For the deeper ResNet-34 model, we achieve an amortized inference time of 28.14 on a batch of 256 encrypted images using 246.78GB of RAM

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arXiv2025

Prismo: A Decision Support System for Privacy-Preserving ML Framework Selection

Machine learning has become a crucial part of our lives, with applications spanning nearly every aspect of our daily activities. However, using personal information in machine learning applications has sparked significant security and privacy concerns about user data. To address these challenges, different privacy-preserving machine learning (PPML) frameworks have been developed to protect sensitive information in machine learning applications. These frameworks generally attempt to balance design trade-offs such as computational efficiency, communication overhead, security guarantees, and scalability. Despite the advancements, selecting the optimal framework and parameters for specific deployment scenarios remains a complex and critical challenge for privacy and security application developers. We present Prismo, an open-source recommendation system designed to aid in selecting optimal parameters and frameworks for different PPML application scenarios. Prismo enables users to explore a comprehensive space of PPML frameworks through various properties based on user-defined objectives. It supports automated filtering of suitable candidate frameworks by considering parameters such as the number of parties in multi-party computation or federated learning and computation cost constraints in homomorphic encryption. Prismo models every use case into a Linear Integer Programming optimization problem, ensuring tailored solutions are recommended for each scenario. We evaluate Prismo's effectiveness through multiple use cases, demonstrating its ability to deliver best-fit solutions in different deployment scenarios.

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arXiv2025

PrivSpike: Employing Homomorphic Encryption for Private Inference of Deep Spiking Neural Networks

Deep learning has become a cornerstone of modern machine learning. It relies heavily on vast datasets and significant computational resources for high performance. This data often contains sensitive information, making privacy a major concern in deep learning. Spiking Neural Networks (SNNs) have emerged as an energy-efficient alternative to conventional deep learning approaches. Nevertheless, SNNs still depend on large volumes of data, inheriting all the privacy challenges of deep learning. Homomorphic encryption addresses this challenge by allowing computations to be performed on encrypted data, ensuring data confidentiality throughout the entire processing pipeline. In this paper, we introduce PRIVSPIKE, a privacy-preserving inference framework for SNNs using the CKKS homomorphic encryption scheme. PRIVSPIKE supports arbitrary depth SNNs and introduces two key algorithms for evaluating the Leaky Integrate-and-Fire activation function: (1) a polynomial approximation algorithm designed for high-performance SNN inference, and (2) a novel scheme-switching algorithm that optimizes precision at a higher computational cost. We evaluate PRIVSPIKE on MNIST, CIFAR-10, Neuromorphic MNIST, and CIFAR-10 DVS using models from LeNet-5 and ResNet-19 architectures, achieving encrypted inference accuracies of 98.10%, 79.3%, 98.1%, and 66.0%, respectively. On a consumer-grade CPU, SNN LeNet-5 models achieved inference times of 28 seconds on MNIST and 212 seconds on Neuromorphic MNIST. For SNN ResNet-19 models, inference took 784 seconds on CIFAR-10 and 1846 seconds on CIFAR-10 DVS. These results establish PRIVSPIKE as a viable and efficient solution for secure SNN inference, bridging the gap between energy-efficient deep neural networks and strong cryptographic privacy guarantees while outperforming prior encrypted SNN solutions.

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arXiv2025

FHEON: A Configurable Framework for Developing Privacy-Preserving Neural Networks Using Homomorphic Encryption

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. Homomorphic Encryption (HE) has emerged as a promising solution to this problems, allowing computations on encrypted data without decryption. Despite its potential, existing approaches to integrate HE into neural networks are often limited to specific architectures, leaving 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, a configurable framework for developing privacy-preserving convolutional neural network (CNN) models for inference using HE. FHEON introduces optimized and configurable implementations of privacy-preserving CNN layers including convolutional layers, average pooling layers, ReLU activation functions, and fully connected layers. These layers are configured using parameters like input channels, output channels, kernel size, stride, and padding to support arbitrary CNN architectures. 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 build 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. Additionally, FHEON operates within a practical memory budget requiring not more than 42.3 GB for VGG-16.

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arXiv2025

Gotta Hash 'Em All! Speeding Up Hash Functions for Zero-Knowledge Proof Applications

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 profiles, 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 $10 \times$, with the leading design achieving a $23 \times$ 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.

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arXiv2025

Activate Me!: Designing Efficient Activation Functions for Privacy-Preserving Machine Learning with Fully Homomorphic Encryption

The growing adoption of machine learning in sensitive areas such as healthcare and defense introduces significant privacy and security challenges. These domains demand robust data protection, as models depend on large volumes of sensitive information for both training and inference. Fully Homomorphic Encryption (FHE) presents a compelling solution by enabling computations directly on encrypted data, maintaining confidentiality across the entire machine learning workflow. However, FHE inherently supports only linear operations, making it difficult to implement non-linear activation functions, essential components of modern neural networks. This work focuses on designing, implementing, and evaluating activation functions tailored for FHE-based machine learning. We investigate two commonly used functions: the Square function and Rectified Linear Unit (ReLU), using LeNet-5 and ResNet-20 architectures with the CKKS scheme from the OpenFHE library. For ReLU, we assess two methods: a conventional low-degree polynomial approximation and a novel scheme-switching technique that securely evaluates ReLU under FHE constraints. Our findings show that the Square function performs well in shallow networks like LeNet-5, achieving 99.4% accuracy with 128 seconds per image. In contrast, deeper models like ResNet-20 benefit more from ReLU. The polynomial approximation yields 83.8% accuracy with 1,145 seconds per image, while our scheme-switching method improves accuracy to 89.8%, albeit with a longer inference time of 1,697 seconds. These results underscore a critical trade-off in FHE-based ML: faster activation functions often reduce accuracy, whereas those preserving accuracy demand greater computational resources.

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arXiv2024

AMAZE: Accelerated MiMC Hardware Architecture for Zero-Knowledge Applications on the Edge

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$\times$. 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.

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