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Preventing velocity-based linkage attacks in location-aware applications

Published: 04 November 2009 Publication History

Abstract

Mobile devices with positioning capabilities allow users to participate in novel and exciting location-based applications. For instance, users may track the whereabouts of their acquaintances in location-aware social networking applications, e.g., GoogleLatitude. Furthermore, users can request information about landmarks in their proximity. Such scenarios require users to report their coordinates to other parties, which may not be fully trusted. Reporting precise locations may result in serious privacy violations, such as disclosure of lifestyle details, sexual orientation, etc. A typical approach to preserve location privacy is to generate a cloaking region (CR) that encloses the user position. However, if locations are continuously reported, an attacker can correlate CRs from multiple timestamps to accurately pinpoint the user position within a CR.
In this work, we protect against linkage attacks that infer exact locations based on prior knowledge about maximum user velocity. Assume user u who reports two consecutive cloaked regions A and B. We consider two distinct protection scenarios: in the first case, the attacker does not have information about the sensitive locations on the map, and the objective is to ensure that u can reach some point in B from any point in A. In the second case, the attacker knows the placement of sensitive locations, and the objective is to ensure that u can reach any point in B from any point in A. We propose spatial and temporal cloaking transformations to preserve user privacy, and we show experimentally that privacy can be achieved without significant quality of service deterioration.

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  • (2023)LPP2KL: Online Location Privacy Protection Against Knowing-and-Learning Attacks for LBSsIEEE Transactions on Computational Social Systems10.1109/TCSS.2022.314207810:1(234-245)Online publication date: Feb-2023
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      cover image ACM Conferences
      GIS '09: Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
      November 2009
      575 pages
      ISBN:9781605586496
      DOI:10.1145/1653771
      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 ACM 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: 04 November 2009

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      Author Tags

      1. location privacy
      2. location-aware social networks

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      View all
      • (2024)Protecting Vehicle Location Privacy with Contextually-Driven Synthetic Location GenerationProceedings of the 32nd ACM International Conference on Advances in Geographic Information Systems10.1145/3678717.3691211(29-41)Online publication date: 29-Oct-2024
      • (2024)Learning Location From Shared Elevation Profiles in Fitness Apps: A Privacy PerspectiveIEEE Transactions on Mobile Computing10.1109/TMC.2022.321814823:1(581-596)Online publication date: Jan-2024
      • (2023)LPP2KL: Online Location Privacy Protection Against Knowing-and-Learning Attacks for LBSsIEEE Transactions on Computational Social Systems10.1109/TCSS.2022.314207810:1(234-245)Online publication date: Feb-2023
      • (2023)Permission Method for Use of Smartphone Location Data with Emphasis on Users' Understanding2023 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops)10.1109/PerComWorkshops56833.2023.10150409(398-403)Online publication date: 13-Mar-2023
      • (2023)A Novel Deception-Based Scheme to Secure the Location Information for IoBT EntitiesIEEE Access10.1109/ACCESS.2023.324413811(15540-15554)Online publication date: 2023
      • (2022)Location-privacy preserving partial nearby friends querying in urban areasData & Knowledge Engineering10.1016/j.datak.2022.102006139:COnline publication date: 1-May-2022
      • (2021)A Hybrid Spatiotemporal Attack in Continuous LBS Queries2021 IEEE International Conferences on Internet of Things (iThings) and IEEE Green Computing & Communications (GreenCom) and IEEE Cyber, Physical & Social Computing (CPSCom) and IEEE Smart Data (SmartData) and IEEE Congress on Cybermatics (Cybermatics)10.1109/iThings-GreenCom-CPSCom-SmartData-Cybermatics53846.2021.00039(174-181)Online publication date: Dec-2021
      • (2021)Protecting Locations with Differential Privacy against Location-Dependent Attacks in Continuous LBS Queries2021 IEEE 20th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)10.1109/TrustCom53373.2021.00065(379-386)Online publication date: Oct-2021
      • (2021)Garbage In, Garbage Out: Poisoning Attacks Disguised With Plausible Mobility in Data AggregationIEEE Transactions on Network Science and Engineering10.1109/TNSE.2021.31039198:3(2679-2693)Online publication date: 1-Jul-2021
      • (2021)Protecting Spatiotemporal Event Privacy in Continuous Location-Based ServicesIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2019.296331233:8(3141-3154)Online publication date: 1-Aug-2021
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