Adversaries and countermeasures in privacy-enhanced urban sensing systems


De Cristofaro, E.; Di Pietro, R. Adversaries and countermeasures in privacy-enhanced urban sensing systems. IEEE Systems Journal, Special Issue on Security and Privacy of Complex Systems. 2013 June; 7 (2): 311-322.


Modern digital society is increasingly dependent on the availability of massive amounts of information. It relies on the interconnection of heterogeneous components and encompasses assorted actors, entities, systems, and a variety of (often mobile) computing devices. Revolutionary computing paradigms, such as People-Centric Urban Sensing, have focused on the seamless collection of meaningful data from a large number of devices. The increasing complexity of deployed urban systems and related infrastructures, along with the growing amount of information collected, prompts a number of challenging security and privacy concerns. In this paper, we explore a number of scenarios where nodes of an Urban Sensing system are subject to individual queries. In this setting, multiple users and organizations (e.g. infrastructure operators) co-exist, but they may not trust each other to the full extent. As a result, we address the problem of protecting (i) secrecy of reported data and (ii) confidentiality of query interests from the prying eyes of malicious entities. We introduce a realistic network model and study different adversarial models and strategies, distinguishing between resident and non-resident adversaries, considering both randomly distributed and local attackers. For each of them, we propose a distributed privacy-preserving technique and evaluate its effectiveness via analysis and simulation. Our techniques are tunable, trading off the level of privacy assurance with a small overhead increase, and independent from the complexity of the underlying infrastructures. We additionally provide a relevant improvement of data reliability and availability, while relying only on standard symmetric cryptography. The practicality of our proposals is demonstrated both analytically and experimentally.

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