(d,l)-diversity: Privacy Preservation for Publication Numerical Sensitive Data
Mohammad Reza Zare Mirakabad · 2012
ε,m)-anonymity considers ε as the interval to define similarity between two values, and m as the level of privacy protection.For example {40,60} satisfies (ε,m)-anonymity but {40,50,60} doesn't, for ε=15 and m=2.We show that protection in {40,50,60} sensitive values of an equivalence class is not less (if don't say more) than {40,60}.Therefore, although (ε,m)anonymity has well studied publication of numerical sensitive values, it fails to address proximity in the right way.Accordingly, we introduce a revised principle which solve this problem by introducing (δ,l)-diversity principle.Surprisingly, in contrast with (ε,m)-anonymity, the proposed principle respects monotonicity property which makes it adoptable to be exploited in other anonymity principles.