On the Development of a Metric for Quality of Information Content over Anonymised Data-Sets

Abstract

We propose a framework for measuring the impact of data anonymisation and obfuscation in information theoretic and data mining terms. Privacy functions often hamper machine learning but obscuring the classification functions. We propose to use Mutual Information over non-Euclidean spaces as a means of measuring the distortion induced by privacy function and following the same principle, we also propose to use Machine Learning techniques in order to quantify the impact of said obfuscation in terms of further data mining goals.