Accelerating LTV Based Homomorphic Encryption in Reconfigurable Hardware

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HEAX: An Architecture for Computing on Encrypted Data (2019, background): “Note that parameter sets corresponding to 2 11 (or lower) are almost never used in practice due to the multiplication depth of 1 (or zero).”
A Survey on Homomorphic Encryption Schemes (2017, methodology): “Other improvements on hardware implementations of NTRU-like FHE schemes are more recently published in [Liu and Wu 2015; Doröz et al. 2015b].”
HEAX: High-Performance Architecture for Computation on Homomorphically Encrypted Data in the Cloud (2019, background): “Several works [58, 24, 27, 59, 20, 21] focus on improving the performance of YASHE [9] and LTV [53] schemes or their variants.”
HEPCloud: An FPGA-Based Multicore Processor for FV Somewhat Homomorphic Function Evaluation (2018, background): “Hence, it is not surprising that during the recent years several publications [8], [13], [14], [15], [26], [28], [31], [33], [40], [41] have reported re-sults on hardware-based acceleration of different FHE and SHE schemes or their central operations.”
cuHE: A Homomorphic Encryption Accelerator Library (2015, background): “For example, in [12] an NTT conversion costs 0.”