Table 4 Existing De-identification preserving privacy measures and its limitations in big data
From: Big data privacy: a technological perspective and review
S.No | Privacy measure | Definitions | Limitations | Computational complexity |
|---|---|---|---|---|
1 | K-anonymity | It is a framework for constructing and evaluating algorithms and systems that release information such that released information limits what can be revealed about the properties of entities that are to be protected | Homogeneity-attack, background knowledge | |
2 | L-diversity | An equivalence class is said to have L-diversity if there are at least “well-represented” values for the sensitive attribute. A table is said to have L-diversity if every equivalence class of the table has L-diversity | L-diversity may be difficult and unnecessary to achieve and L-diversity is insufficient to prevent attribute disclosure | O((n2)/k) |
3 | T-closeness | An equivalence class is said to have T-closeness if the distance between the distribution of a sensitive attribute in this class and the distribution of the attribute in the whole table is no more than a threshold t. A table is said to have t-closeness if all equivalence classes have t-closeness | T-closeness requires that the distribution of a sensitive attribute in any equivalence class is close to the distribution of a sensitive attribute in the overall table | 2O(n)O(m) [36] |