Location data from mobile devices is a sensitive yet valuable commodity for location-based services and advertising. We investigate the intrinsic value of location data in the context of strong privacy, where location information is only available from end users via purchase. We present an algorithm to compute the expected value of location data from a user, without access to the specific coordinates of the location data point. We use decision-theoretic techniques to provide a principled way for a potential buyer to make purchasing decisions about private user location data. We illustrate our approach in three scenarios: the delivery of targeted ads specific to a user's home location, the estimation of traffic speed, and the prediction of location. In all three cases, the methodology leads to quantifiably better purchasing decisions than competing approaches.
As people carry and interact with their connected devices, they create spatiotemporal data that can be harnessed by them and others to generate a variety of insights. Proposals have been made for creating markets for personal data1 rather than for people either to provide their behavioral data freely or to refuse sharing. Some of these proposals are specific to location data.6 Several studies have explored the price that people would seek for sharing their GPS data.5, 13, 9 However, little has been published on determining the value of location data from a buyer's point of view. For instance, a Wall Street Journal blog says10:
"What groceries you buy, what Facebook posts you 'like' and how you use GPS in your car:
Companies are building their entire businesses around the collection and sale of such data. The problem is that no one really knows what all that information is worth. Data isn't a physical asset like a factory or cash, and there aren't any official guidelines for assessing its value."
We present a principled method for computing the value of spatiotemporal data from the perspective of a buyer. Knowledge of this value could guide pursuit of the most informative data and would provide insights about potential markets for location data.
No entries found