While a number of data privacy techniques have been proposed in the recent years, a few frameworks have been suggested for the implementation of the data privacy process. Most of the proposed approaches are tailored towards implementing a specific data privacy algorithm but not the overall data privacy engineering and design process. Therefore, as a contribution, this study proposes SIED (Specification, Implementation, Evaluation, and Dissemination), a conceptual framework that takes a holistic approach to the data privacy engineering procedure by looking at the specifications, implementation, evaluation, and finally, dissemination of the privatized data sets.
Archive for September, 2013
Kato Mivule and Claude Turner
It is obligatory that organizations by law safeguard the privacy of individuals when handling data sets containing personal identifiable information (PII). Nevertheless, during the process of data privatization, the utility or usefulness of the privatized data diminishes. Yet achieving the optimal balance between data privacy and utility needs has been documented as an NP-hard challenge. In this study, we investigate data privacy and utility preservation using KNN machine learning classification as a gauge.