首页|线云隐私攻击算法的并行加速研究

线云隐私攻击算法的并行加速研究

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线云定位方法能保护场景隐私,但也存在被隐私攻击算法破解的风险.该攻击算法能从线云恢复近似点云,但其计算效率较低.针对该问题,提出了一种并行优化算法,并对其运行时间和加速比进行了分析.具体来说,分别采用SPMD模式和流水线模式实现了CPU多核并行和 GPGPU 并行.然后,进一步结合数据并行模式实现了异构计算,以达到最高的并行度.实验结果表明,并行优化算法加速比最大为 15.11,最小为 8.20;相比原算法,并行优化算法的还原点云相对误差控制在原误差的 0.4%以内,保证了算法的精度.该研究对线云隐私攻击算法以及其他密度估计问题、不同场景下的线云隐私保护算法等有重要意义和参考价值.
Research on parallel acceleration of line cloud privacy attack algorithm
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.

line cloud privacy securityheterogeneous computingparallel processingprivacy attack algorithmspeedup ratio

郭宸良、阎少宏、宗晨琪

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华北理工大学理学院,河北 唐山 063210

线云隐私安全 异构计算 并行化处理 隐私攻击算法 加速比

国家自然科学基金

U20A20179

2024

计算机工程与科学
国防科学技术大学计算机学院

计算机工程与科学

CSTPCD北大核心
影响因子:0.787
ISSN:1007-130X
年,卷(期):2024.46(4)
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