Optimized privacy-preserving cnn inference with fully homomorphic encryption

D Kim, C Guyot - IEEE Transactions on Information Forensics …, 2023 - ieeexplore.ieee.org
Inference of machine learning models with data privacy guarantees has been widely studied
as privacy concerns are getting growing attention from the community. Among others, secure …

: Towards Secure and Lightweight Deep Learning as a Medical Diagnostic Service

X Liu, Y Zheng, X Yuan, X Yi - … : 26th European Symposium on Research in …, 2021 - Springer
The striking progress of deep learning paves the way towards intelligent and quality medical
diagnostic services. Enterprises deploy such services via the neural network (NN) inference …

Context-aware hybrid encoding for privacy-preserving computation in IoT devices

H Khalili, HJ Chien, A Hass… - IEEE Internet of Things …, 2023 - ieeexplore.ieee.org
Recent years have witnessed a surge in hybrid IoT-cloud applications where an end user
distributes the desired computation between the IoT and cloud nodes. While achieving …

Batch Inference on Deep Convolutional Neural Networks With Fully Homomorphic Encryption Using Channel-By-Channel Convolutions

JH Cheon, M Kang, T Kim, J Jung… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Secure Machine Learning as a Service (MLaaS) is a viable solution where clients seek
secure ML computation delegation while protecting sensitive data. We propose an efficient …

[PDF][PDF] PP-Stream: Toward High-Performance Privacy-Preserving Neural Network Inference via Distributed Stream Processing

Q Liu, Q Huang, X Chen, S Wang… - Proceedings of the …, 2024 - researchgate.net
Privacy preservation is critical for neural network inference, which often involves
collaborative execution of different parties to make predictions on sensitive data based on …