A Privacy Reinforcement Approach against De-identified Dataset

Protection of individual privacy has been a key issue for the corresponding data dissemination. Nowadays powerful search utilities increase the re-identification risk by easier information collection as well as validation than before. Despite there usually performs certain de-identified process, att...

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Main Authors: Ci-Wei Lan, Yi-Hui Chen, Grandison, T., Huang, A. F. M., Jen-Yao Chung, Li-Feng Tseng
Format: Conference Proceeding
Language:English
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data mask
Decision support systems
microdata protection
Privacy
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Yi-Hui Chen
Grandison, T.
Huang, A. F. M.
Jen-Yao Chung
Li-Feng Tseng
description Protection of individual privacy has been a key issue for the corresponding data dissemination. Nowadays powerful search utilities increase the re-identification risk by easier information collection as well as validation than before. Despite there usually performs certain de-identified process, attackers may recognize someone from released dataset with which attacker-owned information is matched. In this paper, we propose an approach to mitigate the identity disclosure problem by generating plurals in a given dataset. The approach leverages decision tree to help selection of quasi-identifier and several masking techniques can be employed for privacy reinforcement. In addition to different privacy metrics applicability, the approach can achieve better trade-off between data integrity and privacy protection through flexible data masking.
doi_str_mv 10.1109/ICEBE.2011.25
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subjects Conferences
data mask
Decision support systems
microdata protection
Privacy
quasi-identifier
title A Privacy Reinforcement Approach against De-identified Dataset
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