Anonymization Technique Preserving Privacy against Inference Attack using Statistical Background Knowledge
Youngha Ryu, Kangsoo Jung, Seog Park · 2011
Today, released data contains sensitive information that may compromise individual privacy. To prevent privacy violations, several protection models have been developed. However, these models have limitations against new attack models such as background knowledge attack. Our research presents an attack model using statistical background knowledge and introduces the safety state to preserve privacy. Through experiments, we show practicability and efficiency of our technique in guaranteeing individual privacy.