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Privacy-preserving distributed monitoring of visit quantities

Published: 06 November 2012 Publication History

Abstract

The organization and planning of services (e.g. shopping facilities, infrastructure) requires quantitative information about the number of customers and their frequency of visiting. In this paper we present a framework which enables the collection of quantitative visit information for arbitrary sets of locations in a distributed and privacy-preserving way. While trajectory analysis is typically performed on a central database requiring the transmission of sensitive personal movement information, the main principle of our approach is the local processing of movement data. Only aggregated statistics are transmitted anonymously to a central coordinator, which generates the global statistics. In this paper we present our approach including the methodical background that enables distributed data processing as well as the architecture of the framework.

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Cited By

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  • (2022)Privacy-Preserving Aggregate Mobility Data Release: An Information-Theoretic Deep Reinforcement Learning ApproachIEEE Transactions on Information Forensics and Security10.1109/TIFS.2022.315236117(849-864)Online publication date: 2022
  • (2018)Privacy-preserving Wi-Fi AnalyticsProceedings on Privacy Enhancing Technologies10.1515/popets-2018-00102018:2(4-26)Online publication date: 1-Apr-2018
  • (2017)What Does The Crowd Say About You? Evaluating Aggregation-based Location PrivacyProceedings on Privacy Enhancing Technologies10.1515/popets-2017-00432017:4(156-176)Online publication date: 10-Oct-2017
  • Show More Cited By

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cover image ACM Conferences
SIGSPATIAL '12: Proceedings of the 20th International Conference on Advances in Geographic Information Systems
November 2012
642 pages
ISBN:9781450316910
DOI:10.1145/2424321

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 06 November 2012

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Author Tags

  1. local inference
  2. privacy
  3. trajectory stream analysis

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Overall Acceptance Rate 257 of 1,238 submissions, 21%

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  • (2022)Privacy-Preserving Aggregate Mobility Data Release: An Information-Theoretic Deep Reinforcement Learning ApproachIEEE Transactions on Information Forensics and Security10.1109/TIFS.2022.315236117(849-864)Online publication date: 2022
  • (2018)Privacy-preserving Wi-Fi AnalyticsProceedings on Privacy Enhancing Technologies10.1515/popets-2018-00102018:2(4-26)Online publication date: 1-Apr-2018
  • (2017)What Does The Crowd Say About You? Evaluating Aggregation-based Location PrivacyProceedings on Privacy Enhancing Technologies10.1515/popets-2017-00432017:4(156-176)Online publication date: 10-Oct-2017
  • (2016)Privacy-friendly mobility analytics using aggregate location dataProceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems10.1145/2996913.2996971(1-10)Online publication date: 31-Oct-2016
  • (2015)Privacy Preserving Centralized Counting of Moving ObjectsAGILE 201510.1007/978-3-319-16787-9_6(91-103)Online publication date: 24-Apr-2015

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