Name
Abstract | ||
|---|---|---|
Advances in technology have provided ways to monitor and measure driving behavior. Recently, this technology has been applied to usage-based automotive insurance policies that offer reduced insurance premiums to policy holders who opt-in to automotive monitoring. Several companies claim to measure only speed data, which they further claim preserves privacy. However, we have developed an algorithm - elastic pathing - that successfully tracks drivers' locations from speed data. The algorithm tracks drivers by assuming a start position, such as the driver's home address (which is typically known to insurance companies), and then estimates the possible routes by fitting the speed data to map data. To demonstrate the algorithm's real-world applicability, we evaluated its performance with driving datasets from central New Jersey and Seattle, Washington, representing suburban and urban areas. We are able to estimate destinations with error within 250 meters for 17% of the traces and within 500 meters for 24% of the traces in the New Jersey dataset, and with error within 250 and 500 meters for 15.5% and 27.5% of the traces, respectively, in the Seattle dataset. Our work shows that these insurance schemes enable a substantial breach of privacy. |
Year | Venue | DocType |
|---|---|---|
2017 | CoRR | Journal |
Volume | Citations | PageRank |
abs/1710.06932 | 0 | 0.34 |
References | Authors | |
0 | 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 | 5 | 1.15 |
| Janne Lindqvist | 6 | 685 | 42.58 |