A Framework for Privacy Quantification: Measuring the Impact of Privacy Techniques Through Mutual Information, Distance Mapping, and Machine Learning

In this paper, we propose to investigate how the effects of privacy techniques can be practically assessed in the specific context of data anonymization, and present some possible tools for measuring the effects of such anonymization. We develop an approach using mutual information for measuring the information content in any dataset, including over non-Euclidean data spaces, by means of mapping non-Euclidean distances to a Euclidean space. We further evaluate the proposed approach over toy datasets composed of timestamped GPS traces, and attempt to quantify the information content loss created by three state-of-the-art anonymization approaches. The results allow for an objective quantification of the effects of the k-anonymity and differential privacy algorithms, and illustrate on the toy data used, that such privacy techniques have very non-linear effects on the information content of the data.