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Which cloak dresses you best?: comparing location cloaking methods for mobile users

Published: 04 September 2017 Publication History

Abstract

Location cloaking methods enable the protection of private location data. Different temporal and spatial approaches to cloak a specific user location (e.g., k-anonymity) have been suggested. Besides the research focusing on functionality, little work has been done on how cloaking methods should be presented to the user. In practice common location referencing services force the user to either accept or deny exact positioning. Therefore, users are not enabled to regulate private location information on a granular level. To improve the usage of location cloaking methods and foster location privacy protection, we conducted a user study (N = 24) comparing different visualized cloaking methods. The results of our lab study revealed a preference for visualizations using already known and well understood real world entities. Thus, the usage of simple and real world concepts can contribute to the application of cloaking methods and subsequently to location privacy protection.

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      cover image ACM Conferences
      MobileHCI '17: Proceedings of the 19th International Conference on Human-Computer Interaction with Mobile Devices and Services
      September 2017
      874 pages
      ISBN:9781450350754
      DOI:10.1145/3098279
      Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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      Publication History

      Published: 04 September 2017

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      Author Tags

      1. location cloaking
      2. location obfuscation
      3. location privacy
      4. mobile location sharing
      5. user study

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      • European Commission

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      MobileHCI '17 Paper Acceptance Rate 45 of 224 submissions, 20%;
      Overall Acceptance Rate 202 of 906 submissions, 22%

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