Understand
Today, the publication of microdata poses a privacy threat.
- Vast research has striven to define the privacy condition that microdata should satisfy before it is released, and devise algorithms to anonymize the data so as to achieve this condition.
- Yet, no method proposed to date explicitly bounds the percentage of information an adversary gains after seeing the published data for each sensitive value therein.
- This paper introduces beta-likeness, an appropriately robust privacy model for microdata anonymization, along with two anonymization schemes designed therefor, the one based on generalization, and the other based on perturbation.