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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.
Free-Space Optical Communications for 6G Wireless Networks: Challenges, Opportunities, and Prototype Validation
In this study, we perform video signal transmissions via an FPGA-based FSO communication prototype to investigate the feasibility of an FSO link with a distance of up to 20~km.
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.
