MapReducing GEPETO or Towards Conducting a Privacy Analysis on Millions of Mobility Traces
Résumé
GEPETO (for GEoPrivacy-Enhancing Toolkit) is a flexible software that can be used to visualize, sanitize, perform inference attacks and measure the utility of a particular geolocated dataset. The main objective of GEPETO is to enable a data curator (e.g., a company, a governmental agency or a data protection authority) to design, tune, experiment and evaluate various sanitization algorithms and inference attacks as well as visualizing the following results and evaluating the resulting trade-off between privacy and utility. In this paper, we propose to adopt the MapReduce paradigm in order to be able to perform a privacy analysis on large scale geolocated datasets composed of millions of mobility traces. More precisely, we design and implement a complete MapReduce-based approach to GEPETO. Most of the algorithms used to conduct an inference attack (such as sampling, kMeans and DJ-Cluster) represent good candidates to be abstracted in the MapReduce formalism. These algorithms have been implemented with Hadoop and evaluated on a real dataset. Preliminary results show that the MapReduced versions of the algorithms can efficiently handle millions of mobility traces.