Privacy Preservation Techniques for Sequential Data Releasing

Abstract

Privacy violation is a serious issue that must be considered when datasets are released for public use. To address this issue, a well-known privacy preservation model, l-Diversity, is proposed. Unfortunately, l-Diversity is generally proposed to address privacy violation issues in datasets that are focused on performing one-time data releasing. For this reason, l-Diversity could be inadequate to preserve the privacy data if datasets are dynamic and released at all times. To rid this vulnerability of l-Diversity, a new privacy preservation model for sequential data releasing to be proposed in this work, so called as ε-Error and l-Diversity. Aside from privacy preservation constraints, the complexity and the data utility are also maintained in the privacy preservation constraint of the proposed model.