Anonymizing Data with Relational and Transaction Attributes
Abstract
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.
Cite
Text
Poulis et al. "Anonymizing Data with Relational and Transaction Attributes." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2013. doi:10.1007/978-3-642-40994-3_23Markdown
[Poulis et al. "Anonymizing Data with Relational and Transaction Attributes." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2013.](https://mlanthology.org/ecmlpkdd/2013/poulis2013ecmlpkdd-anonymizing/) doi:10.1007/978-3-642-40994-3_23BibTeX
@inproceedings{poulis2013ecmlpkdd-anonymizing,
title = {{Anonymizing Data with Relational and Transaction Attributes}},
author = {Poulis, Giorgos and Loukides, Grigorios and Gkoulalas-Divanis, Aris and Skiadopoulos, Spiros},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2013},
pages = {353-369},
doi = {10.1007/978-3-642-40994-3_23},
url = {https://mlanthology.org/ecmlpkdd/2013/poulis2013ecmlpkdd-anonymizing/}
}