Homomorphic Encryption

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Homomorphic encryption allows a service to process encrypted data without seeing the underlying inputs, then return results that the data owner can decrypt. Research applies schemes such as CKKS for approximate arithmetic and TFHE for Boolean operations to neural-network and Transformer inference, federated learning, retrieval, and sensing. To reduce cost, systems use SIMD packing, polynomial approximations, fewer bootstraps, specialized hardware, and joint design of cryptographic protocols and model architectures. The central challenge is making encrypted computation practical at useful accuracy and scale while controlling latency, memory, communication, and privacy leakage.

Latest papers

41 papers3 papers in the last four weeks, down 62% on the four weeks before. 0.0% of all new papers.

Towards Deep Encrypted Training: Low-Latency, Memory-Efficient, and High-Throughput Inference for Privacy-Preserving Neural Networks
Apr 18, 2026Nges Brian Njungle, Eric Jahns, Michel A. KinsyEfficient Neural Network Inference Memory-Efficient Inference
STAM Center, Ira A. Fulton Schools of Engineering · Arizona State University, Tempe, AZ 85281, USA
Fully Homomorphic Encryption on Llama 3 model for privacy preserving LLM inference
Apr 14, 2026Anes Abdennebi, Nadjia Kara, Laaziz LahlouPrivacy-Preserving Language Models LLM Inference
Software and IT Engineering Department, École de Technologie Supérieure, Montreal, Quebec, Canada.
GPU Acceleration of Sparse Fully Homomorphic Encrypted DNNs
Apr 13, 2026Lara D'Agata, Carlos Agulló-Domingo, Óscar Vera-López +7Sparse Matrix Multiplication Homomorphic Encryption
University of Glasgow, UK · University of Murcia, Spain · AMD +1
AEGIS: Scaling Long-Sequence Homomorphic Encrypted Transformer Inference via Hybrid Parallelism on Multi-GPU Systems
Apr 3, 2026Zhaoting Gong, Ran Ran, Fan Yao +1Efficient Transformer Inference Homomorphic Encryption
North Carolina State University, Raleigh, NC, USA · University of Central Florida, Orlando, FL, USA
Privacy-Preserving Iris Recognition: Performance Challenges and Outlook
Mar 27, 2026Christina Karakosta, Lian Alhedaithy, William J. KnottenbeltBiometric Identification Homomorphic Encryption
Department of Computing, Imperial College London
Efficient Encrypted Computation in Convolutional Spiking Neural Networks with TFHE
Mar 25, 2026Longfei Guo, Pengbo Li, Ting Gao +3Privacy-Preserving ML Efficient Neural Network Inference
School of Cyber Science and Engineering, Huazhong University of Science and Technology, Wuhan, China · Center for Mathematical Science, Huazhong University of Science and Technology, Wuhan, China · School of Mathematics and Statistics, Huazhong University of Science and Technology, Wuhan, China +3
mmFHE: mmWave Sensing with End-to-End Fully Homomorphic Encryption
Mar 23, 2026Tanvir Ahmed, Yixuan Gao, Adnan Armouti +1mmWave Radar Sensing Remote Sensing
Cornell Tech
Meeting in the Middle: A Co-Design Paradigm for FHE and AI Inference
Mar 20, 2026Bernardo Magri, Benjamin Marsh, Paul GebheimHomomorphic Encryption Privacy-Preserving ML
University of Manchester · Sei Labs · University of Portsmouth
Integrating Homomorphic Encryption and Synthetic Data in FL for Privacy and Learning Quality
Mar 3, 2026Yenan Wang, Carla Fabiana Chiasserini, Elad Michael SchillerPrivacy-Preserving ML Federated Learning
Computer Science and Engineering, Chalmers University of Technology, Gothenburg, Sweden
Cerium: A Multi-GPU Framework for Terabyte-Scale Encrypted Inference
Dec 12, 2025Siddharth Jayashankar, Joshua Kim, Michael B. Sullivan +2Homomorphic Encryption GPU Kernel Optimization
Carnegie Mellon University · UT Austin · NVIDIA
Practical and Private Hybrid ML Inference with Fully Homomorphic Encryption
Sep 1, 2025Sayan Biswas, Philippe Chartier, Akash Dhasade +7Privacy-Preserving ML Homomorphic Encryption
EPFL · Inria · IRMAR +3

Apr 18, 2026J/K move · Enter open · S save

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

Nges Brian Njungle, Eric Jahns, Michel A. Kinsy

STAM Center, Ira A. Fulton Schools of Engineering · Arizona State University, Tempe, AZ 85281, USA

Efficient Neural Network Inference Memory-Efficient Inference Homomorphic Encryption Privacy-Preserving ML

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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