Paper 2026/047

SoK of Private Deep Neural Network Inference with Approximate Fully Homomorphic Encryption

Abstract

Deep neural networks (DNNs), a hot topic in this decade, are already solving many practical problems previously unchallenged. There are clear use cases of strong requirements for privacy protection in DNN models and input data. Fully Homomorphic Encryption (FHE) schemes provide privacy by enabling operations upon encrypted data with post-quantum security, at the expense of vast data size increase. Overwhelming execution times and memory sizes currently limit DNN inference with FHE to severely reduced models and datasets. In this article, we thoroughly review the state of the art and the state of the practice around this topic, and identify the current challenges remaining to enable efficient DNN inference with FHE in production-sized use cases, along with the most promising trends to address them. Advancing upon previous review articles in the literature, our focus is specifically on deep learning inference on top of high-performance hardware. From our analysis, we set what we consider to be an ideal private inference system for DNNs, capturing notions already present in the literature and generalizing them.