A Privacy Measure for Data Disclosure

Closeness is described as a privacy measure and its advantages are illustrated through examples and experiments on a real dataset. In this Paper the closeness can be verified by giving different values for N and T. Government agencies and other organizations often need to publish micro data, e. g. , medical data or census data, for research and other purposes. Typically, such data are stored in a table, and each record (row) corresponds to one individual. Generally if we want to publish micro data A common anonymization approach is generalization, which replaces quasi-identifier values with values that are less-specific but semantically consistent. As a result, more records will have the same set of quasi-identifier values. An equivalence class of an anonymized table is defined to be a set of records that have the same values for the quasi-identifiers To effectively limit disclosure, the disclosure risk of an anonymized table is to be measured. To this end, k-anonymity is introduced as the property that each record is indistinguishable with at least k-1 other records with respect to the quasi-identifier i. e. , k-anonymity requires that each equivalence class contains at least k records. While k-anonymity protects against identity disclosure, it is insufficient to prevent attribute disclosure. To address the above limitation of k-anonymity, a new notion of privacy,