Efficient Personalized Privacy Preservation Using Anonymization.

The k-anonymity privacy for publishing micro data requires that each equivalence class contains at least k records. Many authors have studied that k-anonymity cannot prevent attribute disclosure. The technique of l-diversity has been introduced to address this; l-diversity requires that each equivalence class must have at least well-represented values for every sensitive attribute. In this paper, we show that l-diversity has many limitations. In particular, it is not necessary or sufficient to prevent attribute disclosure. Motivated by these limitations, we propose a new method to detect privacy which is called as closeness. We first present the base model t-closeness, which includes the distribution of sensitive attributes in any of the equivalence classes is near to the distribution of the attribute in the overall table (i.e., the difference between the two given distributions should be no more than threshold value t). tcloseness that gives higher utility. We present our method for designing a distance measure between given two probability distributions and give two distance measures. Here we discuss the method for implementing closeness as a privacy concern and illustrate its advantages through examples and experiments.