A user-oriented anonymization mechanism for public data
DPM'10/SETOP'10 Proceedings of the 5th international Workshop on data privacy management, and 3rd international conference on Autonomous spontaneous security
| Hi-index | 0.00 |
Two different approaches to defining a notion of database privacy, the generalization method and the perturbation method, have been independently studied. These two approaches are significantly different, making it hard to compare related research. In this paper, we propose a unified model that is based on the perturbation method, but which is applicable to generalized data sets. In particular, this model applies the notion of differential privacy to data sets that satisfy $k$-anonymity. We demonstrate this approach through a simple case study. This is a first step towards a common notion for protecting database privacy.