Today people increasingly have the opportunity to opt-in to "usage-based" automotive insurance programs for reducing insurance premiums. In these programs, participants install devices in their vehicles that monitor their driving behavior, which raises some privacy concerns. Some devices collect fine-grained speed data to monitor driving habits. Companies that use these devices claim that their approach is privacy-preserving because speedometer measurements do not have physical locations. However, we show that with knowledge of the user's home location, as the insurance companies have, speed data is sufficient to discover driving routes and destinations when trip data is collected over a period of weeks. To demonstrate the real-world applicability of our approach we applied our algorithm, elastic pathing, to data collected over hundreds of driving trips occurring over several months. With this data and our approach, we were able to predict trip destinations to within 250 meters of ground truth in 10 of the traces and within 500 meters in 20 of the traces. This result, combined with the amount of speed data that is being collected by insurance companies, constitutes a substantial breach of privacy because a person's regular driving pattern can be deduced with repeated examples of the same paths with just a few weeks of monitoring.
Map building with robots is interesting because there are often no exact ground truths, only location estimates. @cite_2 describe a method that can assemble maps from arbitrary distance estimates to and from landmarks. The problem of deducing a traveled path from only speed data and a starting point is, in some ways, the reverse of this map building problem: we already have the map, but have difficulty identifying landmarks (in this case turns) from just speed data.
@cite_2
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A major problem in map building is due to the imprecision of sensor measures. In this paper we propose a technique, called elastic correction, for correcting the dead-reckoning errors made during the exploration of an unknown environment by a robot capable of identifying landmarks. Knowledge of the environment being acquired is modelled by a relational graph whose vertices and arcs represent, respectively, landmarks and inter-landmark routes. Elastic correction is based on an analogy between this graph and a mechanical structure: the map is regarded as a truss where each route is an elastic bar and each landmark a node. Errors are corrected as a result of the deformations induced from the forces arising within the structure as inconsistent measures are taken. The uncertainty on odometry is modelled by the elasticity parameters characterizing the structure.