A Study on Differential Privacy for Data Base with Correlation

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The differential privacy is the technology of adding noise to an answer for the given query. The differential privacy for data base with correlated data records, however, has not get much attention. In this research, we find the counter intuitive phenomena that an adversary with small amount of background knowledge about correlations can get more privacy information than the adversary with big amount of these knowledge. Then, we build the Bayesian privacy model which explains this kind of phenomena and improve this situation. We also show the approximation algorithm that gives us the proper parameter value of Laplace distribution employed in differential privacy which make the provability of information leakage less than previously determined threshold.In addition, we investigated the problems which are caused by added noise in differential privacy.