Privacy Preservation System for Multiple Attributes
TL;DR
The system proposes a framework that ensures data utility while minimizing privacy risks, and provides a mechanism to generate and securely re-identify summary tables derived from sensitive datasets, ensuring a balance between accessibility and confidentiality.
Abstract
Privacy preservation and data utility are two important key factors for publishing the data of multiple sensitive attributes. Traditional anonymization methods often fail to protect against re-identification risks in complex datasets. The proposed system uses semantic L-diversity technique to partition the data into the different buckets. Slicing technique is used to partition the data into multiple sensitive tables along with the quasi table. This approach also uses a bucket id to keep the associations among the sensitive tables and quasi table. Additionally, the framework provides a mechanism to generate and securely re-identify summary tables derived from sensitive datasets, ensuring a balance between accessibility and confidentiality. The system proposes a framework that ensures data utility while minimizing privacy risks. The system aims to provide a solution for organizations to publish valuable data responsibly without compromising individual privacy