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(k, ε)-Anonymity: An anonymity model for thwarting similarity attack | IEEE Conference Publication | IEEE Xplore

(k, ε)-Anonymity: An anonymity model for thwarting similarity attack


Abstract:

Existing anonymity models rarely consider the semantic similarity between sensitive values, so they cannot thwart similarity attack. To solve the problem, this paper prop...Show More

Abstract:

Existing anonymity models rarely consider the semantic similarity between sensitive values, so they cannot thwart similarity attack. To solve the problem, this paper proposes a (k, ε)-anonymity model which requires that each equivalence class in anonymous dataset satisfy k-anonymity constraints. At the same time, any two sensitive values in the same equivalence class are not ε-similar. The paper also proposes a (k, ε)-KACA algorithm. Experimental results show that the anonymous data satisfy(k, ε)-anonymity has higher diversity than that satisfy k-anonymity model, so (k, ε)-anonymity model can protect privacy more effective than k-anonymity model.
Date of Conference: 13-15 December 2013
Date Added to IEEE Xplore: 17 February 2014
Electronic ISBN:978-1-4799-1282-7
Conference Location: Beijing, China

References

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