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 algorithm can recover approximate point clouds from line clouds, but Us 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 multicore parallelism and the GPGPU parallelism are implemented 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 optimization algorithm is 15. 11, and the minimum is 8. 20. Additionally, compared to the original algorithm, the parallel 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. [ABSTRACT FROM AUTHOR]