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Table 1 Different existing privacy techniques

From: Machine learning concepts for correlated Big Data privacy

S. no Privacy measure Definition Limitations
1 k-anonymity This is a mechanism for testing algorithms so that published data restricts what can be disclosed about the properties of entities that are to be secured [1, 2] Homogeneity-attack, background knowledge [2]
2 l-diversity The equivalence class is also said to l-diversity if the sensitive attribute has at least “l-well-represented” values [2, 3] Insufficient to prevent attribute disclosure [2]
3 t-closeness The table is said to have t-closeness if the distribution of the entire attribute in the whole table is no more than a threshold t [4] Complex distribution of a sensitive attribute [4]
4 Differential privacy It is a mechanism to provide an interface between user and database, which protects individual data with the highest mathematical guarantee [5] In the case of data diversity, it includes too much noise, which reduces the data utility [5]