L-diversity on K-anonymity with External Database for Improving Privacy Preserving Data Publishing

Abstract

The data must be secure and measurable at the public when it releases to view. The data table produces personal information and sensitive values. They are maintained for secrecy, the anonymity is the best method to protect the data. There are many anonymity methods to protect the data. k -anonymity is one method to protect the data. The problem in kanonymity method is if data set increases then utility decreases. Also kanonymity data is possible to many attacks like Homogeneity Attack, Background Knowledge Attack. The l diversity is another method to protect the data. Main advantage of ldiversity is the data set increases then the data utility also increases. Based on above advantage, we applied l -diversity concept in k-anonymity applied external data set and we evaluate high efficiency dataset. It shows the l diversity reduces the data losses in k anonymity data sets when data point moves any size.