A Study on Data Perturbation Techniques in Privacy Preserving Data Mining

نویسندگان

  • Nimpal Patel
  • Shreya Patel
چکیده

Student, Dept. Of Computer Engineering, Grow More Faculty of Engineering Himatnagar, Gujarat, India Asst. Professor, Dept. of Computer Engineering, Grow More Faculty of Engineering Himatnagar, Gujarat, India ---------------------------------------------------------------------***--------------------------------------------------------------------Abstract-In recent years, the data mining techniques have met a serious challenge due to the increased concerning and worries of the privacy, that is, protecting the privacy of the critical and sensitive data. Data perturbation is a popular technique for privacy preserving data mining. The major challenge of data perturbation is balancing privacy protection and data quality, which normally considered as a pair of contradictive factors. Geometric data perturbation technique is a combination of Rotation, Translation and Noise addition perturbation technique. It is especially useful for data owners to publish data while preserving privacy –sensitive information. Typical examples include publishing micro data for research purpose or outsourcing the data to the third party that provides data mining services. In this paper we try to explore the latest trends in Geometric data perturbation technique.

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تاریخ انتشار 2016