Movement Data Anonymity through Generalization

TitleMovement Data Anonymity through Generalization
Publication TypeConference Paper
Year of Publication2009
AuthorsAndrienko N, Andrienko G, Giannotti F, Monreale A, Pedreschi D
Conference NameProceedings of 2nd SIGSPATIAL ACM GIS 2009 International Workshop on Security and Privacy in GIS and LBS
Date Published11/2009
Conference LocationSeattle, WA, USA
Project[Project Phase 1] Visual Analytics methods to support the spatiotemporal analysis of movements in a physical space in particular in a geographical space
Abstract

In recent years, spatio-temporal and moving objects databases have gained considerable interest, due to the di usion of mobile devices (e.g., mobile phones, RFID devices and GPS devices) and of new applications, where the discovery of consumable, concise, and applicable knowledge is the key
step. Clearly, in these applications privacy is a concern, since models extracted from this kind of data can reveal the behavior of group of individuals, thus compromising their privacy.
Movement data present a new challenge for the privacy-preserving data mining community because of their spatial and temporal characteristics.
In this position paper we briey present an approach for the generalization of movement data that can be adopted for obtaining k-anonymity in spatio-temporal datasets; specifically, it can be used to realize a framework for publishing of spatio-temporal data while preserving privacy.
We ran a preliminary set of experiments on a real-world trajectory dataset, demonstrating that this method of generalization of trajectories preserves the clustering analysis results.

URL http://visualanalytics.de/sites/default/files/upload/publications/springl09.pdf