A General Survey on Methods of Privacy Preserving Data Mining

K. Sowjanya

Abstract: Background/Objectives: The process of removing interesting patterns or knowledge from a huge amount of information is known as Data Mining. Now a day?s information like PAN No data collection is ubiquitous, and every transaction is recorded somewhere. The subsequent information sets can comprise of terabytes or even pet bytes of information, so efficiency and scalability is the primary consideration of most data mining algorithms. Increasing data collection, along with the influx of analysis tools capable of handling huge volumes of information, has led to privacy concerns. Protecting private data is an important concern for society, several laws now require explicit consent prior to analysis of an individual?s data, but its importance is not limited to individuals: corporations might also need to protect their information?s privacy, even though sharing it for analysis could benefit the company. Clearly, the trade-off between sharing information about analysis and keeping it secret to preserve corporate trade secrets and customer privacy is a growing challenge. In recent years, with the explosive development in Internet, data storage and data processing technologies, privacy preservation has been one of the greater concerns in data mining.Findings: A variety of techniques and approaches have been presented and developed for privacy preserving information mining. Methods: This paper provides a basic survey of different privacy preserving data mining methods and analyses the representative techniques for privacy preserving information mining.

Keywords: PPDM, Randomization, K-Anonymity, Association Rule.