Title :
Achieving P-Sensitive K-Anonymity via Anatomy
Author :
Sun, Xiaoxun ; Wang, Hua ; Li, Jiuyong ; Ross, David
Author_Institution :
Dept. of Math. & Comput., Univ. of Southern Queensland, Toowoomba, QLD, Australia
Abstract :
Privacy-preserving data publishing is to protect sensitive information of individuals in published data while the distortion ratio of the data is minimized. One well-studied approach is the k-anonymity model. Recently, several authors have recognized that k-anonymity cannot prevent attribute disclosure. To address this privacy threat, one solution would be to employ p-sensitive k-anonymity, a novel paradigm in relational data privacy, which prevents sensitive attribute disclosure, p-sensitive k-anonymity partitions the data into groups of records such that each group has at least p distinct sensitive values. Existing approaches for achieving p-sensitive k-anonymity are mostly generalization-based. In this paper, we propose a novel permutation-based approach called anatomy to release the quasi-identifier and sensitive values directly in two separate tables. Combined with a grouping mechanism, this approach not only protects privacy, but captures a large amount of correlation in the microdata. We develop a top-down algorithm for computing anatomized tables that obey the insensitive k-anonymity privacy requirement, and minimize the error of reconstructing the microdata. Extensive experiments confirm that anatomy allows significantly more effective data analysis than the conventional publication methods based on generalization.
Keywords :
data privacy; relational databases; anatomized table computation; error minimization; grouping mechanism; p-sensitive k-anonymity partition; permutation-based approach; privacy-preserving data publishing; relational data privacy; sensitive attribute disclosure prevention; sensitive information protection; top-down algorithm; Anatomy; Data engineering; Data privacy; Databases; Hospitals; Information science; Mathematics; Protection; Publishing; Sun;
Conference_Titel :
e-Business Engineering, 2009. ICEBE '09. IEEE International Conference on
Conference_Location :
Macau
Print_ISBN :
978-0-7695-3842-6
DOI :
10.1109/ICEBE.2009.34