Data Privacy

Organizations collect vast amounts of information on individuals, and at the same time they have access to ever-increasing levels of computational power. Although this conjunction of information and power provides great benefits to society, it also threatens individual privacy. Balancing the effectiveness of data mining against the need for true anonymity presents many challenges. It is difficult to estimate the risk of disclosure since it is difficult to guess intruder background knowledge. It is also difficult to assess information loss arising from the de-identification of data, since the loss is strongly dependent on user needs. Data anonymization is often driven by policy, but privacy legislation is often unclear. Finally, different data models require different privacy approaches.

Our research team investigates the privacy protection problem based on a specific data model. The two main research directions of the data privacy group at NKU are: Privacy Models and Algorithms for Microdata and Privacy in Social Networks.