In the strive for privacy preservation, it is important that the information in a dataset retains as much quality as possible. Defining and measuring the loss of information after privacy has been preserved proves difficult, however. Techniques have been developed to measure the information quality of a dataset for a variety of anonymization techniques including Generalization, Suppression, and Randomization. Some measures analyze the data, while others analyze the outputted data mining results from tasks such as Clustering and Classification. This survey discusses a collection of information measures, and issues surrounding their usage and limitations.
|Number of pages||8|
|Journal||International Journal of Computer Theory and Engineering|
|Publication status||Published - Feb 2015|