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Data Transformation and Data Transitive Techniques for Protecting Sensitive Data in Privacy Preserving Data Mining

机译：隐私保护数据挖掘中保护敏感数据的数据转换和数据传递技术

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摘要

Privacy Preserving Data mining is a new research area in the field of data mining. It greatly deals with the side effects of the data mining techniques. With the help of data mining techniques people can analyze and extract the hidden patterns from the large data set. In many situations, the retrieved hidden knowledge provides the confidential information. This confidential information may be misused for variety of purposes. This situation raises the need for privacy and security issues. The main objective of privacy preserving data mining is, extracting die knowledge available in the data; at the same time the individual's privacy Should be protected. It also protects the data owner against mishandling or disclosure of the data. Several techniques and algorithms are required for maintaining the secrecy of data in order to limit the extraction of confidential patterns. There are many techniques are available for protecting the sensitive data in the database. Some of them are statistical, cryptographic, randomization methods, k-anonymity model, 1-diversity and etc [2-5, 7]. In this research work, we have developed two new perturbative masking techniques known as data transformation technique and data transitive technique. These techniques are used for protecting sensitive data in the form of modifying the sensitive data. In order to find the best technique, we have compared the two techniques with the existing technique micro aggregation. Experimental result shows that the data transformation technique gives the better result.
机译：隐私保护数据挖掘是数据挖掘领域中的一个新研究领域。它极大地处理了数据挖掘技术的副作用。借助数据挖掘技术，人们可以分析并从大型数据集中提取隐藏的模式。在许多情况下，检索到的隐藏知识会提供机密信息。此机密信息可能会出于各种目的而被滥用。这种情况引起了对隐私和安全问题的需求。隐私保护数据挖掘的主要目的是提取数据中可用的知识；同时应保护个人隐私。它还可以保护数据所有者免遭错误处理或泄露数据。需要几种技术和算法来维护数据的保密性，以限制机密模式的提取。有许多技术可用于保护数据库中的敏感数据。其中一些是统计，密码，随机化方法，k-匿名模型，1-多样性等[2-5、7]。在这项研究工作中，我们开发了两种新的扰动掩蔽技术，称为数据转换技术和数据传递技术。这些技术以修改敏感数据的形式用于保护敏感数据。为了找到最佳技术，我们将这两种技术与现有技术的微观聚合进行了比较。实验结果表明，数据转换技术取得了较好的效果。

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