Computational efficiency of k-anonymization incorporating clustering

• Main Authors: Netshiunda, Fhulufhelo Emmanuel, Emmanuel, Netshiunda Fhulufhelo
• Format: Others
• Language: en
• Published: 2020
• Online Access: https://hdl.handle.net/10539/30171

A research report submitted in partial fulfillment of the requirements for the degree of Master of Science in the field of e-Science in the School of Computer Science and Applied Mathematics, University of the Witwatersrand, Johannesburg, 2020 === Data publicizing pose a threat of disclosing data subjects associating them to their personal sensitive information. k-anonymization is a practical method used to anonymize datasets to be made publicly available. The k-anonymization hides identities of data subjects by ensuring that every record of a publicized dataset has at least k �� 1 (k being a natural number) other records similar to it with respect to a set of attributes called quasi-identifiers. To minimize information loss, a clustering technique is often used to group similar records before k-anonymization is applied. Processing both the clustering and the k-anonymization using current algorithms is computationally expensive. It is within this framework that this research focuses on parallel implementation of the k-anonymization algorithm which incorporates clustering to achieve time effective computations === CK2020