The hardness of (ε, m)-anonymity

Abstract

When a table containing individual data is published, disclosure of sensitive information should be prohibitive. (ε, m)-anonymity was a new anonymization principle for preservation of proximity privacy, in publishing numerical sensitive data. It is shown to be NP-Hard to (ε, m)-anonymize a table minimizing the number of suppressed cells. Extensive performance study verified our findings that our algorithm is significantly better than the traditional algorithms presented in the paper[1]. © 2013 Springer-Verlag Berlin Heidelberg.