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Computing Value of Spatiotemporal Information

delete2020-08-21
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PRE
AI
H
Heba El-Sagheer Aly *
J
John Krumm
G
Gireeja Ranade
E
Eric Horvitz
DOI:10.1145/3410387delete
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摘要

摘要

En 中文
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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期刊

Communications of the ACM 封面图
Communications of the ACM
IF:
12.2
论文数:
1.2W
被引数:
3.7W

机构

A
amazon.com
学者数:
698
论文数: 505
被引数: 8
University of California System 封面图
University of California System
学者数:
37.5W
论文数: 33.7W
被引数: 6.6K
M
Microsoft
学者数:
3.0K
论文数: 2.7K
被引数: 7
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