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Towards a privacy threat model for public displays

Published: 23 June 2015 Publication History

Abstract

While public displays have proliferated in many areas, passersby frequently do not perceive the contents shown as useful. One way to provide relevant content is to show personalized information or to let users personalize displays interactively. However, this approach raises privacy concerns. To gain a better understanding of these concerns, we carried out a study assessing the applicability of an existing threat model to public displays. We report on key outcomes, propose an extended privacy threat model tailored to interactive public displays, and provide an assessment of the importance of different privacy threats in various application scenarios. Engineers of interactive displays may use our findings to systematically analyze privacy requirements and threats, which in turn may lead to privacy-aware designs that can positively affect user attitude and display usage.

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  1. Towards a privacy threat model for public displays

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    cover image ACM Conferences
    EICS '15: Proceedings of the 7th ACM SIGCHI Symposium on Engineering Interactive Computing Systems
    June 2015
    316 pages
    ISBN:9781450336468
    DOI:10.1145/2774225
    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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    Published: 23 June 2015

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    Author Tags

    1. STRIDE
    2. privacy
    3. public display
    4. study
    5. threat

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    EICS '15 Paper Acceptance Rate 19 of 64 submissions, 30%;
    Overall Acceptance Rate 73 of 299 submissions, 24%

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