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Found 5 papers, 1 papers with code
Optimizing Layerwise Polynomial Approximation for Efficient Private Inference on Fully Homomorphic Encryption: A Dynamic Programming Approach
Unlike the previous works approximating activation functions uniformly and conservatively, this paper presents a \emph{layerwise} degree optimization of activation functions to aggressively reduce the inference time while maintaining classification accuracy by taking into account the characteristics of each layer.
Adaptive Graph Convolution Module for Salient Object Detection
Salient object detection (SOD) is a task that involves identifying and segmenting the most visually prominent object in an image.
OpenFHE: Open-Source Fully Homomorphic Encryption Library
Fully Homomorphic Encryption (FHE) is a powerful cryptographic primitive that enables performing computations over encrypted data without having access to the secret key.
High-Precision Bootstrapping for Approximate Homomorphic Encryption by Error Variance Minimization
The Cheon-Kim-Kim-Song (CKKS) scheme (Asiacrypt’17) is one of the most promising homomorphic encryption (HE) schemes as it enables privacy-preserving computing over real (or complex) numbers.
Privacy-Preserving Machine Learning with Fully Homomorphic Encryption for Deep Neural Network
Previous PPML schemes replace non-arithmetic activation functions with simple arithmetic functions instead of adopting approximation methods and do not use bootstrapping, which enables continuous homomorphic evaluations.
