TY - GEN
T1 - XColor
T2 - 26th IEEE International Conference on Data Engineering, ICDE 2010
AU - Wang, Ting
AU - Liu, Ling
PY - 2010
Y1 - 2010
N2 - As a severe threat in anonymized data publication, proximity breach is gaining increasing attention. Such breach occurs when an attacker learns with high confidence that the sensitive information of a victim associates with a set of semantically proximate values, even though not sure about the exact one. Recently (ε, δ)-dissimilarity [14] has been proposed as an effective countermeasure against general proximity attack. In this paper, we present a detailed analytical study on the fulfillment of this principle, derive criteria to efficiently test its satisfiability for given microdata, and point to a novel anonymization model, XCOLOR, with theoretical guarantees on both operation efficiency and utility preservation.
AB - As a severe threat in anonymized data publication, proximity breach is gaining increasing attention. Such breach occurs when an attacker learns with high confidence that the sensitive information of a victim associates with a set of semantically proximate values, even though not sure about the exact one. Recently (ε, δ)-dissimilarity [14] has been proposed as an effective countermeasure against general proximity attack. In this paper, we present a detailed analytical study on the fulfillment of this principle, derive criteria to efficiently test its satisfiability for given microdata, and point to a novel anonymization model, XCOLOR, with theoretical guarantees on both operation efficiency and utility preservation.
UR - https://www.scopus.com/pages/publications/77952752270
U2 - 10.1109/ICDE.2010.5447910
DO - 10.1109/ICDE.2010.5447910
M3 - Conference contribution
AN - SCOPUS:77952752270
SN - 9781424454440
T3 - Proceedings - International Conference on Data Engineering
SP - 960
EP - 963
BT - 26th IEEE International Conference on Data Engineering, ICDE 2010 - Conference Proceedings
Y2 - 1 March 2010 through 6 March 2010
ER -