Practical Anonymization for Protecting Privacy in Combinatorial Maps

Dandan Chu, Yidong Li, Tao Wang, Lei Zhang, Hong Shen

研究成果: Conference contribution同行評審

1 引文 斯高帕斯(Scopus)

摘要

Combinatorial Map (CM) is becoming increasingly popular due to its power in modeling topological structures with subdivided objects, which is widely used in the fields of social network, computer vision, social media and so on. However, due to its specific structural properties, an unprotected release of a combinatorial map may cause the identity disclosure problem, which is a major privacy breach revealing the identification of entities with certain background knowledge known by an adversary. In this paper, we discuss the privacy preserving problem in publishing private combinatorial maps. We first formalize a specific anonymizing model to deal with dart-related attacks, and discuss an efficient metric to quantify information loss incurred in the perturbation. Then we propose an efficient method for the dart anonymization problem to prevent a CM from the attack. Our approaches are efficient and practical, and have been validated by extensive experiments on two sets of synthetic data.

原文English
主出版物標題Proceedings - 15th International Conference on Parallel and Distributed Computing, Applications and Technologies, PDCAT 2014
發行者IEEE Computer Society
頁面119-123
頁數5
ISBN(電子)9781479983346
DOIs
出版狀態Published - 31 7月 2015
對外發佈
事件15th International Conference on Parallel and Distributed Computing, Applications and Technologies, PDCAT 2014 - Hong Kong, China
持續時間: 9 12月 201411 12月 2014

出版系列

名字Parallel and Distributed Computing, Applications and Technologies, PDCAT Proceedings
2015-July

Conference

Conference15th International Conference on Parallel and Distributed Computing, Applications and Technologies, PDCAT 2014
國家/地區China
城市Hong Kong
期間9/12/1411/12/14

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