Читать книгу Artificial Intelligence and Data Mining Approaches in Security Frameworks - Группа авторов - Страница 30

To extract the pertinent knowledge from large volumes of data and to protect all sensitive information of that database, we use privacy preserving data mining (PPDM). These techniques are created with the aim to confirm the protection of sensitive data so that privacy can be reserved with the efficient performance of all data mining operations. There are two classes of privacy concerned data mining techniques:

1 Data privacy

2 Information privacyModification of database for the protection of sensitive data of the individuals, we use data privacy technique. If there is a requirement for the modification of sensitive knowledge that can be deduced from the database, information privacy technique is preferred. To provide privacy to input, data privacy is preferable, whereas for providing privacy to output, the technique of information privacy is used. To reserve personal information from exposure is the main focus of a PPDM algorithm. It relies on the analysis of those mining algorithms that are attained during data privacy. Main objective of Privacy Preserving Data Mining is building algorithms that convert the original data in some useful means, so that there is no visibility of private data and knowledge even after a successful mining process. Privacy laws would allow the access in the case that some related satisfactory benefit is found resulting from the access.