Explosive growth in data storing and data processing technologies has led to the creation of large databases that record unprecedented amount of information. Consequently, with the increase in data storage and processing, concerns about information privacy have emerged. Data mining, with its promise to efficiently discover valuable non-obvious information from large databases, is particularly sensitive to privacy concerns. In recent years, data mining has also endeavored to become compatible with privacy.
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Organizations provide assurance of individual privacy and data will be used only for the a well defined purpose. However, it is the common practice of an organization to use individual data for secondary purpose. By secondary purpose, it means that data is being used for which they were not collected initially. Many organizations sell the data to other organizations, which use these data for their own purposes. Thus, the data gets exposed to a number of parties including collectors, owners, users and miners; the privacy of individual is being questioned. Fruitful research has been produced by different researchers on the topic of privacy preserving data mining (PPDM). PPDM deals with the problem of learning accurate models over aggregate data, while protecting privacy at the level of individual records.
Recent research in the area of privacy preserving data mining has devoted much effort to determine a trade-off between privacy and the need for knowledge discovery, which is crucial in order to improve decision-making processes and other human activities. Mainly, three approaches are being adopted for privacy preserving data mining namely, heuristic based, cryptographic based and reconstruction based. Heuristic based techniques are mainly adopted in centralized database scenario, whereas cryptographic based technique finds its application in distributed environment. However, reconstruction based algorithms are well accepted in both centralized as well as the distributed environment.
Book: Privacy Preserving Data Mining (Advances in Information Security) by Jaideep Vaidya , Chris Clifton, Michael Zhu
Web resources: Mobile Privacy Preserving Data Mining
Power Point Presentation (PPT): Privacy Preserving Data Mining: Challenges & Opportunities
Privacy Preserving Data Mining
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