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Research on Privacy Protection of Large-Scale Network Data Aggregation Process

机译:大规模网络数据聚合过程的隐私保护研究

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摘要

Data privacy should be protected by law. Based on the analysis of data privacy protection situation at home and abroad, it is proposed that our country can protect data privacy by improving personnel quality, establishing relevant legal system and adopting technical prevention strategies. These strategies have certain guiding significance for exploring data privacy protection suitable for our national conditions. In order to solve the privacy protection problem in the process of large-scale network data aggregation, this paper proposes an Privacy Protection Algorithms (PPA) based on large-scale network data aggregation for the shortcomings of the existing standard large-scale network data aggregation algorithm with low time efficiency and poor reversibility. Converting the original network database into a large-scale network data aggregation form, performing network compression according to the Hamming weight of each network vector after conversion, using the matrix column vector to perform an AND operation, and calculating the support degree of the candidate set, thereby obtaining frequent itemsets. Experimental results show that compared with the original algorithm, the algorithm can improve the time efficiency while ensuring the false positive rate, has good reversibility and security, and is more practical.
机译:数据隐私应受到法律保护。在分析国内外数据隐私保护现状的基础上,提出我国可以通过提高人员素质,建立相关法律制度和采取技术防范策略来保护数据隐私。这些策略对于探索适合我国国情的数据隐私保护具有一定的指导意义。为了解决大规模网络数据聚合过程中的隐私保护问题,针对现有标准大规模网络数据聚合的缺点,提出了一种基于大规模网络数据聚合的隐私保护算法(PPA)。算法效率低,可逆性差。将原始网络数据库转换为大规模的网络数据聚合形式,根据转换后每个网络向量的汉明权重进行网络压缩,使用矩阵列向量执行AND运算,并计算候选集的支持度,从而获得频繁的项目集。实验结果表明,与原始算法相比,该算法在保证误报率的同时,提高了时间效率,具有良好的可逆性和安全性,更加实用。

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