A new perspective of privacy protection: Unique distinct l-SR diversity

More and more public data sets which contain information about individuals are published in recent years. The urgency to reduce the risk of privacy disclosure from such data sets makes the approaches of privacy protection for data publishing widely employed. There are two popular models for privacy protection: k-anonymity and l-diversity. k-anonymity focuses on reducing the probability of identifying a particular person, which requires that each equivalence class (a set of records with the same identifier attributes) contains at least k records. l-diversity concentrates on reducing the inference from released sensitive attributes. It requires that each equivalence class has at least l “well-represented” sensitive attribute values. In this study, we view the privacy protection problem in a brand new perspective. We proposed a new model, Unique Distinct l-SR diversity based on the sensitivity of private information. Also, we presented two performance measures to evaluate how much sensitive information can be inferred from an equivalence class. l-SR diversity algorithm was implemented to achieve Unique Distinct l-SR diversity. We tested l-SR diversity on one benchmark data set and two synthetic data sets, and compared it with other l-diversity algorithms. The results show that our algorithm achieved better performance on minimizing inference of sensitive information and reached the comparable generalization data quality compared with other data publishing algorithms.