Advertisement
Published in:
2013 | OriginalPaper | Chapter
Anonymizing Data with Relational and Transaction Attributes
Authors : Giorgos Poulis, Grigorios Loukides, Aris Gkoulalas-Divanis, Spiros Skiadopoulos
Published in: Machine Learning and Knowledge Discovery in Databases
Publisher: Springer Berlin Heidelberg
Activate our intelligent search to find suitable subject content or patents.
Select sections of text to find matching patents with Artificial Intelligence. powered by
Select sections of text to find additional relevant content using AI-assisted search. powered by
Publishing datasets about individuals that contain both relational and transaction (i.e., set-valued) attributes is essential to support many applications, ranging from healthcare to marketing. However, preserving the privacy and utility of these datasets is challenging, as it requires
(i)
guarding against attackers, whose knowledge spans both attribute types, and
(ii)
minimizing the overall information loss. Existing anonymization techniques are not applicable to such datasets, and the problem cannot be tackled based on popular, multi-objective optimization strategies. This work proposes the first approach to address this problem. Based on this approach, we develop two frameworks to offer privacy, with bounded information loss in one attribute type and minimal information loss in the other. To realize each framework, we propose privacy algorithms that effectively preserve data utility, as verified by extensive experiments.