Simple data transformation method for privacy preserving data re-publication

As growing interest in data publishing and analysis, privacy preserving data publication has become more important today. When a table containing the sensitive information is published, privacy of each individual should be protected. On the other hand, a data holder also considers minimizing information loss for analysis as long as the privacy is preserved. A few years ago, k-anonymity and l-diversity models have been suggested in order to protect privacy. However, these solutions are limited to static data release. Recently, the m-invariance model has been proposed to apply publication of dynamic environments. However, m-invariance generalization technique causes high information loss. In this paper, we propose a simple and safe anonymization technique without generalization while assuring high data utility in dynamic environments.