Elastic Pathing: Your Speed Is Enough to Track You Presented by Ali

Motivation • Insurance companies claim to collect only speed data

• Attracting users to install monitoring device

• Example: • Progressive: Snapshot device

Problem• It is possible to track you by knowing starting point and

driving speed with timestamps

• Insurance companies know your starting point, home address

• Speed data not considered confidential by insurance companies

Challenge • Possibilities of multiple paths

• Hard to determine turning direction

Elastic Pathing Algorithm: Overview • OpenStreetMap

Elastic Pathing Algorithm: Error Detection • Errors happen when:

• stopping in the midway • speed is too fast to make turns

• Path correction:• Expanding (stretching) • Compressing

• Degree of correction affects the path score

Elastic Pathing: Assumptions• Drivers will stop only at traffic lights and stop signs

• Each vehicle has physical limitation – turning radius

• No vehicle can make turns at high speed

Elastic Pathing Algorithm• Chooses the path with smallest error

• Checks for max. and min speeds

• Makes a “landmark” when speed trace and road data match OR:

• A vehicle has come to a stop at an intersection

• Sorts all possible paths

Elastic Pathing Algorithm: Example

Elastic Pathing Algorithm: Accuracy • New Jersey dataset

• 14% traces with error less than 0.16 miles – 250 m • 24% traces with error less than 0.31 – 500 m

• Seattle dataset• 13% traces with error less than 0.16 miles – 250 m • 26% traces with error less than 0.31 miles – 500 m

Real-world Sample

Conclusion • Accuracy does not go down with trip distance

• The algorithm depends on the driving habits

• Distinguishing between two different roads having similar features

Demo

https://youtu.be/TI6TmwwURoU