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A Review Of Synthetic Data Generation Methods For Privacy Preserving Data Publishing

Journal: International Journal of Scientific & Technology Research (Vol.6, No. 3)

Publication Date:

Authors : ; ;

Page : 95-101

Keywords : Disclosure Control; Data Masking; Inference Control; Privacy Preserving Data Publishing PPDP; Privacy Preserving Data Mining PPDM; Synthetic Data Generation.;

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Due to the technological advancement enormous micro data containing detailed individual information is being collected by both public and private organizations. The demand for releasing this data to public for social and economic welfare is growing. Also the organizations holding the data are under pressure to publish the data for proving their transparency. Since this micro data contains sensitive information about individuals the raw data needs to be sanitized to preserve privacy of the individuals before releasing it to the public. There are different types of data sanitization methods and many techniques are being proposed for Privacy Preserving Data Publishing PPDP of micro data. Synthetic Data Generation is an alternative to data masking techniques for preserving privacy. In this paper different fully and partially synthetic data generation techniques are reviewed and key research gaps are identified which needs to be focused in the future research.

Last modified: 2017-06-11 22:59:54