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On the value of spatiotemporal information: principles and scenarios

Published: 06 November 2018 Publication History
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  • 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 two scenarios: the delivery of targeted ads specific to a user's home location and the estimation of traffic speed. In both cases, the methodology leads to quantifiably better purchasing decisions than competing methods.

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    Cited By

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    • (2023)Data-Driven Digital Mobility TwinsProceedings of the 7th ACM SIGSPATIAL Workshop on Location-based Recommendations, Geosocial Networks and Geoadvertising10.1145/3615896.3628417(4-5)Online publication date: 13-Nov-2023
    • (2022)Sensitivity Analysis of Personal Location Disclosure2022 23rd IEEE International Conference on Mobile Data Management (MDM)10.1109/MDM55031.2022.00032(73-82)Online publication date: Jun-2022
    • (2021)Quantifying Intrinsic Value of Information of TrajectoriesProceedings of the 29th International Conference on Advances in Geographic Information Systems10.1145/3474717.3483912(81-90)Online publication date: 2-Nov-2021
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    cover image ACM Conferences
    SIGSPATIAL '18: Proceedings of the 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
    November 2018
    655 pages
    ISBN:9781450358897
    DOI:10.1145/3274895
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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    Publication History

    Published: 06 November 2018

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    Author Tags

    1. GPS
    2. advertising
    3. crowdsourcing
    4. decision theory
    5. location
    6. traffic
    7. value of information (VOI)

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    SIGSPATIAL '18 Paper Acceptance Rate 30 of 150 submissions, 20%;
    Overall Acceptance Rate 220 of 1,116 submissions, 20%

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    View all
    • (2023)Data-Driven Digital Mobility TwinsProceedings of the 7th ACM SIGSPATIAL Workshop on Location-based Recommendations, Geosocial Networks and Geoadvertising10.1145/3615896.3628417(4-5)Online publication date: 13-Nov-2023
    • (2022)Sensitivity Analysis of Personal Location Disclosure2022 23rd IEEE International Conference on Mobile Data Management (MDM)10.1109/MDM55031.2022.00032(73-82)Online publication date: Jun-2022
    • (2021)Quantifying Intrinsic Value of Information of TrajectoriesProceedings of the 29th International Conference on Advances in Geographic Information Systems10.1145/3474717.3483912(81-90)Online publication date: 2-Nov-2021
    • (2021)Errors in Geotargeted Display AdvertisingProceedings of the ACM on Human-Computer Interaction10.1145/34491665:CSCW1(1-19)Online publication date: 22-Apr-2021
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    • (2020)TinyLinkACM Transactions on Sensor Networks10.1145/341236617:1(1-29)Online publication date: 27-Sep-2020
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