A Privacy Measure for Data Disclosure to Publish Micro Data using (N,T) - Closeness
A. Sunitha, K. Venkata Subba Reddy, B. Vijayakumar · International Journal of Computer Applications · 2012
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 kanonymity protects against identity disclosure, it is insufficient to prevent attribute disclosure.To address the above limitation of k-anonymity, a new notion of privacy, called l-diversity is introduced, which requires that the distribution of a sensitive attribute in each equivalence class has at least l "well represented" values.One problem with ldiversity is that it is limited in its assumption of adversarial knowledge.This assumption generalizes the specific background and homogeneity attacks used to motivate ldiversity.The k-anonymity privacy requirement for publishing micro data requires that each equivalence class contains at least k records.But k-anonymity cannot prevent attribute disclosure.The notion of l-diversity has been proposed to address this; l-diversity requires that each equivalence class has at least l well-represented values for each sensitive attribute.L-diversity has a number of limitations.In particular, it is neither necessary nor sufficient to prevent attribute disclosure.Due to these limitations, a new notion of privacy called "closeness" is proposed.First the base model t-closeness is presented, which requires that the distribution of a sensitive attribute in any equivalence class is close to the distribution of the attribute in the overall table.Then a more flexible privacy model called (n, t)-closeness is proposed.The rationale for using General TermsData and Knowledge based systems.