Computing value of spatiotemporal information
Abstract
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.
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- Computing value of spatiotemporal information
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September 2020
90 pages
Copyright © 2020 ACM.
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 21 August 2020
Published in CACM Volume 63, Issue 9
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