线云隐私攻击算法的并行加速研究OA北大核心CSTPCD

The localization methods based on line cloud can protect scene privacy,but they also face the risk of being cracked by a privacy attack algorithm proposed by Kunal Chelani et al.This attack al-gorithm can recover approximate point clouds from line clouds,but its computational efficiency is low.To address this issue,a parallel optimization algorithm is proposed and evaluated in terms of running time and speedup ratio.Specifically,the CPU multi-core parallelism and the GPGPU parallelism are im-plemented using the SPMD pattern and the pipeline parallel pattern respectively.Furthermore,the data parallel pattern is adopted to implement heterogeneous computing,to achieve the highest degree of parallelism.Experimental results demonstrate that the maximum speedup ratio of the parallel optimiza-tion algorithm is 15.11,and the minimum is 8.20.Additionally,compared to the original algorithm,the parrellel optimization algorithm ensures the relative error of the recovered point clouds within 0.4%of the original error,ensuring the accuracy of the algorithm.This research holds significant importance and reference value for line cloud privacy attack algorithms,as well as for privacy protection algorithms in Line Cloud under different scenarios and other density estimation problems.