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Privacy Preserving Data Mining : Database Security And Privacy

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Privacy Preserving Data Mining

Database Security and Privacy

Jing Wang
250711908

Abstract In recent years, privacy preserving data mining has become a hotspot in data mining. Today, the field of privacy has seen rapid advances in recent years because of the increase in the ability to store data. In particular, recent advances in the data mining field have led to increasing concerns about privacy. However, with the development of technologies, the emerging applications result in an accumulation of abundant personal privacy information, which will easily lead to the violence of personal privacy. Therefore, it is significant to study the privacy preserving data mining methods of new applications. Data mining is the process of extraction of data from a large database. Knowledge Discovery in Database (KDD) is another name of data mining. One of the most important topics in a research community is Privacy Preserving Data Mining (PPDM). It refers to the area of data mining that seeks to safeguard sensitive information from unsolicited or unsanctioned disclosure. The Success of Privacy Preserving data mining algorithms is measured in terms of its performance, data utility, level of uncertainty or resistance to data mining algorithms. The objective of privacy preserving data mining (PPDM) is to find a way to manipulate the dataset, so that the sensitive message can’t be disclosed in data mining.

Keywords: Database, Privacy, Data Mining, PPDM

Introduction
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