Name
Abstract | ||
|---|---|---|
Today, people have the opportunity to opt-in to usage-based automotive insurances for reduced premiums by allowing companies to monitor their driving behavior. Several companies claim to measure only speed data to preserve privacy. With our elastic pathing algorithm, we show that drivers can be tracked by merely collecting their speed data and knowing their home location, which insurance companies do, with an accuracy that constitutes privacy intrusion. To demonstrate the algorithm's real-world applicability, we evaluated its performance with datasets from central New Jersey and Seattle, Washington, representing suburban and urban areas. Our algorithm predicted destinations with error within 250 meters for 14% traces and within 500 meters for 24% traces in the New Jersey dataset (254 traces). For the Seattle dataset (691 traces), we similarly predicted destinations with error within 250 and 500 meters for 13% and 26% of the traces respectively. Our work shows that these insurance schemes enable a substantial breach of privacy. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1145/2632048.2632077 | UbiComp |
Keywords | Field | DocType |
location privacy,miscellaneous,privacy,destination prediction,elastic pathing,usage-based automotive insurance | Intrusion,Computer science,Computer security,Real-time computing,Multimedia,Automotive industry,Destinations | Conference |
Citations | PageRank | References |
14 | 0.72 | 27 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xianyi Gao | 1 | 32 | 2.39 |
| Bernhard Firner | 2 | 494 | 23.49 |
| Shridatt Sugrim | 3 | 62 | 7.58 |
| Victor Kaiser-Pendergrast | 4 | 14 | 1.06 |
| Yulong Yang | 5 | 74 | 4.99 |
| Janne Lindqvist | 6 | 685 | 42.58 |