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Data mining privacy preserving: Research agenda
WIREs Data Mining and Knowledge Discovery ( IF 7.8 ) Pub Date : 2020-10-18 , DOI: 10.1002/widm.1392
Inda Kreso 1 , Amra Kapo 1 , Lejla Turulja
Affiliation  

In the modern days, the amount of the data and information is increasing along with their accessibility and availability, due to the Internet and social media. To be able to search this vast data set and to discover unknown useful data patterns and predictions, the data mining method is used. Data mining allows for unrelated data to be connected in a meaningful way, to analyze the data, and to represent the results in the form of useful data patterns and predictions that help and predict future behavior. The process of data mining can potentially violate sensitive and personal data. Individual privacy is under attack if some of the information leaks and reveals the identity of a person whose personal data were used in the data mining process. There are many privacy‐preserving data mining (PPDM) techniques and methods that have a task to preserve the privacy and sensitive data while providing accurate data mining results at the same time. PPDM techniques and methods incorporate different approaches that protect data in the process of data mining. The methodology that was used in this article is the systematic literature review and bibliometric analysis. This article identifieds the current trends, techniques, and methods that are being used in the privacy‐preserving data mining field to make a clear and concise classification of the PPDM methods and techniques with possibly identifying new methods and techniques that were not included in the previous classification, and to emphasize the future research directions.

中文翻译:

数据挖掘隐私保护:研究议程

如今,由于互联网和社交媒体的出现,数据和信息的数量以及它们的可访问性和可用性都在增加。为了能够搜索这个庞大的数据集并发现未知的有用数据模式和预测,使用了数据挖掘方法。数据挖掘允许无关的数据以有意义的方式连接,分析数据并以有用的数据模式和预测的形式表示结果,以帮助和预测未来的行为。数据挖掘过程可能会破坏敏感和个人数据。如果某些信息泄漏并泄露在数据挖掘过程中使用了其个人数据的人的身份,则个人隐私将受到攻击。有许多隐私保护数据挖掘(PPDM)技术和方法,其任务是保存隐私和敏感数据,同时提供准确的数据挖掘结果。PPDM技术和方法结合了在数据挖掘过程中保护数据的不同方法。本文使用的方法是系统的文献综述和文献计量分析。本文确定了隐私保护数据挖掘领域中正在使用的当前趋势,技术和方法,以对PPDM方法和技术进行清晰,简洁的分类,并可能确定以前版本中未包括的新方法和技术。分类,并强调未来的研究方向。
更新日期:2020-12-17
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