Anonymization by Local Recoding in Data with Attribute Hierarchical Taxonomies

Authors:
Jiuyong Li;Raymond Chi-Wing Wong;Ada Wai-Chee Fu;Jian Pei
Affiliations:
University of South Australia, Adelaide;the Chinese University of Hong Kong, Hong Kong;The Chinese University of Hong Kong, Hong Kong;Simon Fraser Univeristy, Burnaby
Venue:
IEEE Transactions on Knowledge and Data Engineering
Year:
2008

Citing 0
Cited 12

Injecting purpose and trust into data anonymisation

Proceedings of the 18th ACM conference on Information and knowledge management
Anonymization of set-valued data via top-down, local generalization

Proceedings of the VLDB Endowment
eM²: an efficient member migration algorithm for ensuring k-anonymity and mitigating information loss

SDM'10 Proceedings of the 7th VLDB conference on Secure data management
Cloning for privacy protection in multiple independent data publications

Proceedings of the 20th ACM international conference on Information and knowledge management
A semantic information loss metric for privacy preserving publication

DASFAA'10 Proceedings of the 15th international conference on Database Systems for Advanced Applications - Volume Part II
Hiding emerging patterns with local recoding generalization

PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
Data privacy against composition attack

DASFAA'12 Proceedings of the 17th international conference on Database Systems for Advanced Applications - Volume Part I
Information based data anonymization for classification utility

Data & Knowledge Engineering
Privacy preservation by disassociation

Proceedings of the VLDB Endowment
Improvements on a privacy-protection algorithm for DNA sequences with generalization lattices

Computer Methods and Programs in Biomedicine
A privacy framework: indistinguishable privacy

Proceedings of the Joint EDBT/ICDT 2013 Workshops
A general framework for privacy preserving data publishing

Knowledge-Based Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

Individual privacy will be at risk if a published data set is not properly de-identified. k-anonymity is a major technique to de-identify a data set. Among a number of k-anonymisation schemes, local recoding methods are promising for minimising the distortion of a k-anonymity view. This paper addresses two major issues in local recoding k-anonymisation in attribute hierarchical taxonomies. Firstly, we define a proper distance metric to achieve local recoding generalisation with small distortion. Secondly, we propose a means to control the inconsistency of attribute domains in a generalised view by local recoding. We show experimentally that our proposed local recoding method based on the proposed distance metric produces higher quality k-anonymity tables in three quality measures than a global recoding anonymisation method, Incognito, and a multidimensional recoding anonymisation method, Multi. The proposed inconsistency handling method is able to balance distortion and consistency of a generalised view.