arXiv subjects

Eduardo Chielle

Publications and source records attributed to Eduardo Chielle.

5 recordsLinked to original sources

arXiv2026

PAPER: Privacy-Preserving Convolutional Neural Networks using Low-Degree Polynomial Approximations and Structural Optimizations on Leveled FHE

Recent work using Fully Homomorphic Encryption (FHE) has made non-interactive privacy-preserving inference of deep Convolutional Neural Networks (CNNs) possible. However, the performance of these methods remains limited by their heavy reliance on bootstrapping, a costly FHE operation applied across multiple layers that severely slows inference. Moreover, they depend on high-degree polynomial approximations of non-linear activations, which increase multiplicative depth and reduce accuracy by 2-5% compared with plaintext ReLU models. In this work, we close the accuracy gap between FHE-based non-interactive CNNs and their plaintext counterparts while achieving faster inference than existing methods. We propose a quadratic polynomial approximation of ReLU that achieves the theoretical minimum multiplicative depth for non-linear activations, together with a penalty-based training strategy. We further introduce structural optimizations that reduce the required FHE levels in CNNs by a factor of five compared with prior work, allowing us to run deep CNN models under leveled FHE without bootstrapping. To further accelerate inference and recover accuracy typically lost with polynomial approximations, we introduce parameter clustering together with a joint strategy involving data layout and ensemble techniques. Experiments with VGG and ResNet models on CIFAR and Tiny-ImageNet datasets show that our approach achieves up to 4x faster private inference than prior work, with accuracy comparable to plaintext ReLU models.

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arXiv2026

CHEHAB RL: Learning to Optimize Fully Homomorphic Encryption Computations

Fully Homomorphic Encryption (FHE) enables computations directly on encrypted data, but its high computational cost remains a significant barrier. Writing efficient FHE code is a complex task requiring cryptographic expertise, and finding the optimal sequence of program transformations is often intractable. In this paper, we propose CHEHAB RL, a novel framework that leverages deep reinforcement learning (RL) to automate FHE code optimization. Instead of relying on predefined heuristics or combinatorial search, our method trains an RL agent to learn an effective policy for applying a sequence of rewriting rules to automatically vectorize scalar FHE code while reducing instruction latency and noise growth. The proposed approach supports the optimization of both structured and unstructured code. To train the agent, we synthesize a diverse dataset of computations using a large language model (LLM). We integrate our proposed approach into the CHEHAB FHE compiler and evaluate it on a suite of benchmarks, comparing its performance against Coyote, a state-of-the-art vectorizing FHE compiler. The results show that our approach generates code that is $5.3\times$ faster in execution, accumulates $2.54\times$ less noise, while the compilation process itself is $27.9\times$ faster than Coyote (geometric means).

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arXiv2024

CoFHEE: A Co-processor for Fully Homomorphic Encryption Execution (Extended Version)

The migration of computation to the cloud has raised concerns regarding the security and privacy of sensitive data, as their need to be decrypted before processing, renders them susceptible to potential breaches. Fully Homomorphic Encryption (FHE) serves as a countermeasure to this issue by enabling computation to be executed directly on encrypted data. Nevertheless, the execution of FHE is orders of magnitude slower compared to unencrypted computation, thereby impeding its practicality and adoption. Therefore, enhancing the performance of FHE is crucial for its implementation in real-world scenarios. In this study, we elaborate on our endeavors to design, implement, fabricate, and post-silicon validate CoFHEE, a co-processor for low-level polynomial operations targeting Fully Homomorphic Encryption execution. With a compact design area of $12mm^2$, CoFHEE features ASIC implementations of fundamental polynomial operations, including polynomial addition and subtraction, Hadamard product, and Number Theoretic Transform, which underlie most higher-level FHE primitives. CoFHEE is capable of natively supporting polynomial degrees of up to $n = 2^{14}$ with a coefficient size of 128 bits, and has been fabricated and silicon-verified using 55nm CMOS technology. To evaluate it, we conduct performance and power experiments on our chip, and compare it to state-of-the-art software implementations and other ASIC designs.

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arXiv2023

Accelerating Fully Homomorphic Encryption by Bridging Modular and Bit-Level Arithmetic

The dramatic increase of data breaches in modern computing platforms has emphasized that access control is not sufficient to protect sensitive user data. Recent advances in cryptography allow end-to-end processing of encrypted data without the need for decryption using Fully Homomorphic Encryption (FHE). Such computation however, is still orders of magnitude slower than direct (unencrypted) computation. Depending on the underlying cryptographic scheme, FHE schemes can work natively either at bit-level using Boolean circuits, or over integers using modular arithmetic. Operations on integers are limited to addition/subtraction and multiplication. On the other hand, bit-level arithmetic is much more comprehensive allowing more operations, such as comparison and division. While modular arithmetic can emulate bit-level computation, there is a significant cost in performance. In this work, we propose a novel method, dubbed bridging, that blends faster and restricted modular computation with slower and comprehensive bit-level computation, making them both usable within the same application and with the same cryptographic scheme instantiation. We introduce and open source C++ types representing the two distinct arithmetic modes, offering the possibility to convert from one to the other. Experimental results show that bridging modular and bit-level arithmetic computation can lead to 1-2 orders of magnitude performance improvement for tested synthetic benchmarks, as well as one real-world FHE application: a genotype imputation case study.

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arXiv2022

Scalable privacy-preserving cancer type prediction with homomorphic encryption

Machine Learning (ML) alleviates the challenges of high-dimensional data analysis and improves decision making in critical applications like healthcare. Effective cancer type from high-dimensional genetic mutation data can be useful for cancer diagnosis and treatment, if the distinguishable patterns between cancer types are identified. At the same time, analysis of high-dimensional data is computationally expensive and is often outsourced to cloud services. Privacy concerns in outsourced ML, especially in the field of genetics, motivate the use of encrypted computation, like Homomorphic Encryption (HE). But restrictive overheads of encrypted computation deter its usage. In this work, we explore the challenges of privacy preserving cancer detection using a real-world dataset consisting of more than 2 million genetic information for several cancer types. Since the data is inherently high-dimensional, we explore smaller ML models for cancer prediction to enable fast inference in the privacy preserving domain. We develop a solution for privacy preserving cancer inference which first leverages the domain knowledge on somatic mutations to efficiently encode genetic mutations and then uses statistical tests for feature selection. Our logistic regression model, built using our novel encoding scheme, achieves 0.98 micro-average area under curve with 13% higher test accuracy than similar studies. We exhaustively test our model's predictive capabilities by analyzing the genes used by the model. Furthermore, we propose a fast matrix multiplication algorithm that can efficiently handle high-dimensional data. Experimental results show that, even with 40,000 features, our proposed matrix multiplication algorithm can speed up concurrent inference of multiple individuals by approximately 10x and inference of a single individual by approximately 550x, in comparison to standard matrix multiplication.

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