基于数据挖掘的水平分区医疗数据集隐私保护技术

Shivlal Mewada
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引用次数: 4

摘要

通过数据挖掘技术提取有价值的信息。近年来,保护隐私的数据挖掘技术被广泛应用于信息和数据的安全保护。这些技术通过交换、修改和删除功能将原始数据集转换为受保护的数据集。这项技术分为两个步骤。在第一步中,云计算考虑一个服务平台来确定给定数据的最佳水平分区。本文采用k - means++算法,在不泄露集群中心信息的情况下确定云平台上的水平分区。第二步包含数据保护和恢复阶段。第二步,在数据库中加入噪声以保持数据的私密性和语义性。同时,利用种子函数对原始数据库进行保护。使用几个基准医疗数据集评估了所提出技术的有效性。使用加密时间、执行时间、准确性和f-measure参数对结果进行评估。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Data Mining-Based Privacy Preservation Technique for Medical Dataset Over Horizontal Partitioned
The valuable information is extracted through data mining techniques. Recently, privacy preserving data mining techniques are widely adopted for securing and protecting the information and data. These techniques convert the original dataset into protected dataset through swapping, modification, and deletion functions. This technique works in two steps. In the first step, cloud computing considers a service platform to determine the optimum horizontal partitioning in given data. In this work, K-Means++ algorithm is implemented to determine the horizontal partitioning on the cloud platform without disclosing the cluster centers information. The second steps contain data protection and recover phases. In the second step, noise is incorporated in the database to maintain the privacy and semantic of the data. Moreover, the seed function is used for protecting the original databases. The effectiveness of the proposed technique is evaluated using several benchmark medical datasets. The results are evaluated using encryption time, execution time, accuracy, and f-measure parameters.
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