ID 原文 译文
3213 实验仿真结果表明，MR-PARIMIEG 在大数据环境下进行频繁项集挖掘时具有较好的性能表现，适用于对较大规模的数据集进行并行化处理。 Experimental simulation results show that MR-PARIMIEG has better performancewhen mining frequent item sets in the big data environment, and it is suitable for parallel processing of larger data sets.
3214 为了实现数据安全共享的同时减少客户端在数据挖掘过程中的计算成本，基于 BCP 同态加密算法提出了联合委托学习模型及协议。 In order to realize data security sharing and reduce the computing costs of clients in data mining process, ajoint delegation learning model and protocol based on BCP homomorphic encryption algorithm was proposed.
3215 首先，针对决策树模型的安全构造提出了基于虚假记录的隐私保护方法。 Firstly, aprivacy preserving method based on false records was proposed for the security of decision tree model.
3216 其次，根据数据垂直分布与水平分布的情况，基于隐私保护委托点积算法和隐私保护委托求熵算法提出了相应的委托学习协议。 Secondly, in viewof the vertical and horizontal distribution of data, the corresponding delegation learning protocols based on privacy pre-serving delegation dot product algorithm and privacy preserving delegation entropy algorithm was proposed.
3217 最后，给出了委托学习协议及决策树模型结构的安全性证明和性能分析。 Finally, the security proof and the performance analysis of delegation learning protocols and decision tree model structure were given.
3218 结果表明，基于虚假记录的隐私保护方法不会影响最终模型的构建，并且各客户端最终获得的模型与真实数据构建的模型具有一致性。 The results show that the privacy protection method based on false records does not affect the final model construction,and the final model obtained by each client is the same as that constructed by real data.
3219 针对海洋网络节点间计算能力与通信资源的差异性，提出了一种基于海洋网络连通概率的边缘计算节点选取方法。 Considering the differences in computing capacity and communication resources of the maritime networknodes, a maritime network connectivity probability based method was proposed for selecting edge computing servicenodes.
3220 根据海洋近岸与远岸的网络节点密度不同，分别建立 2 种卸载模型。 Because of the different node densities in the near-shore and far-shore scenarios, two offloading models were es-tablished accordingly.
3221 在近岸场景下，提出多节点协同的卸载方法，利用基于海洋多节点协同卸载遗传算法求解； In the near-shore scenario, a multi-node cooperative offloading method was proposed by using thegenetic algorithm based on maritime multi-node cooperative offloading.
3222 在远岸场景下，提出可容错的卸载方法，利用基于分组交叉学习粒子群算法求解。 In the far-shore scenario, a fault-tolerant of-floading method was proposed based on the particle swarm algorithm with grouping cross learning.