Correlation Based Anonymization Using Generalization and Suppression for Disclosure Problems

Data Anonymization Techniques for Preserving Privacy in Public Release Data Model A Technical Review

“…This technique should be applied at the beginning of the anonymization process. This is the strongest type of anonymization technique because there is no way of recovering any information back after anonymization [7]. After the elimination of the joining date attribute, the sample dataset changed as showed in Table 2.…”

Review of Different Privacy Preserving Techniques in PPDP

“…The result of above methods shows that these operation causes a considerable amount of information loss because higher the generalization hierarchy more information loss will be there [1]. Also, suppression causes the elimination of values.…”

“…The main goal of this paper is to share data in a way that privacy is preserved while information loss is kept at least. Data that include Government agencies, University details and Medical history etc., are very necessary for an organization to do analysis and predict trends and patterns, but it may prevent the data owner from sharing the data because of privacy regulations [1]. By doing an analysis of several algorithms of Anonymization such as k-anonymity, l-diversity and tcloseness, one can achieve privacy at minimum loss.…”