Thesis (Ph.D.) - Cairo University - Institute of Statistical Studies and Research - Department of Computer and Information Science

Although data mining is an increasingly important technology for extracting useful knowledge hidden in huge collections of data, there are, however, negative social perceptions about data mining, among which are potential privacy violation and potential discrimination. For this reason, privacy preserving data mining has been introduced for protecting individual privacy. The initial idea of privacy preserving data mining (PPDM) was to extend traditional data mining techniques to work with the data modified to mask sensitive information. The key issues were how to modify the data and how to recover the data mining result from the modified data. Through studying many privacy techniques depending on k-anonymity technique, there are two main problems facing publishers that need to be solved as explained in the next lines: Problem No.1: How to maintain privacy when multiple sensitive attributes are presented in released data because while a data publisher attempts to protect one sensitive attribute may cause disclosure of identity due to another one. Problem No.2: How to operate privacy preserving data mining techniques on original data to maintain privacy using k-anonymity technique without affecting data utility and accuracy

Issued also as CD