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Type: Conference paper
Title: Achieving probabilistic anonymity against one-to-multiple linkage attacks
Author: Shen, H.
Tian, H.
Sang, Y.
Zhang, Z.
Citation: Proceedings: 2013 IEEE 10th International Conference on e-Business Engineering: ICEBE 2013 / A. James, X. Fei, K.-M. Chao, J.-Y. Chung (eds.): p.176-183
Publisher: IEEE
Publisher Place: United States
Issue Date: 2013
Series/Report no.: International Conference on e-Business Engineering
ISBN: 9780769551111
Conference Name: International Conference on e-Business Engineering (10th : 2013 : Coventry, United Kingdom)
Editor: James, A.
Fei, X.
Chao, K.M.
Chung, J.Y.
Statement of
Yingpeng Sang, Hong Shen, Hui Tian, Zonghua Zhang
Abstract: Randomization methods widely applied for privacy-preserving data mining are generally subject to reconstruction attack, linkage attack, and semantic-related attacks. A probabilistic anonymity definition has been proposed in [1] to defend against the linkage attack in which the attacker links the same randomized record to all of the original records. In this paper we name this type of attack as Multiple (original records) to One (randomized record) attack, while focus on another attack that has not been researched before, i.e. One (original record) to Multiple (randomized records) attack. The latter is different from the former in that it does not require the attacker to know the distribution and all values of quasi-identifiers in original records, and thus is easier to be launched by the attacker. To defend against this attack we propose a novel probabilistic anonymity concept different from [1]. We achieve this anonymity goal on a hybrid model combining random projection and random noise addition. We also analyze the security properties of this model against the other common types of attacks. Compared with existing work in randomization, k-anonymity and differential privacy, our work achieves the holistic aim of higher security, higher efficiency and higher data utility, and demonstrates very promising applications in large-scale and high-dimensional data mining in clouds.
Keywords: randomization
differential privacy
data mining
Rights: © 2013 IEEE
DOI: 10.1109/ICEBE.2013.27
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Appears in Collections:Aurora harvest
Computer Science publications

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