Location-based services rise high privacy concerns because they make it possible to collect and infer sensitive information from a person's positions and mobility traces. Many solutions have been proposed to safeguard the users' privacy, at least to a certain extent. However, they generally lacking convincing experimental validation with real human mobility traces. Large databases of real mobility traces are extremely expensive to build or buy. In this paper, we present HUMsim (Human Urban Mobility Simulator), a generator of synthetic but realistic human traces oriented to the experimental validation of privacy solutions. HUMsim generates trajectories that reflect possibly privacy-sensitive habits of people and that, at the same time, account for constraints deriving from a real map. We also validate the soundness of the produced traces by statistically comparing them to real human traces.
|Titolo:||HUMsim: A Privacy-Oriented Human Mobility Simulator|
|Anno del prodotto:||2015|
|Appare nelle tipologie:||4.1 Contributo in Atti di convegno|