Conference item
X-ray refine: Supporting the exploration and refinement of information exposure resulting from smartphone apps
- Abstract:
- Most smartphone apps collect and share information with various first and third parties; yet, such data collection practices remain largely unbeknownst to, and outside the control of, end-users. In this paper, we seek to understand the potential for tools to help people refine their exposure to third parties, resulting from their app usage. We designed an interactive, focus-plus-context display called X-Ray Refine (Refine) that uses models of over 1 million Android apps to visualise a person’s exposure profile based on their durations of app use. To support exploration of mitigation strategies, Refine can simulate actions such as app usage reduction, removal, and substitution. A lab study of Refine found participants achieved a high-level understanding of their exposure, and identified data collection behaviours that violated both their expectations and privacy preferences. Participants also devised bespoke strategies to achieve privacy goals, identifying the key barriers to achieving them.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
Actions
Access Document
- Files:
-
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(Preview, Accepted manuscript, pdf, 1.2MB, Terms of use)
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- Publisher copy:
- 10.1145/3173574.3173967
Authors
- Publisher:
- Association for Computing Machinery
- Host title:
- ACM CHI Conference on Human Factors in Computing Systems (CHI 2018)
- Journal:
- ACM CHI Conference on Human Factors in Computing Systems (CHI 2018) More from this journal
- Publication date:
- 2018-04-21
- Acceptance date:
- 2018-02-12
- DOI:
- Keywords:
- Pubs id:
-
pubs:827817
- UUID:
-
uuid:1160b96f-09c5-4181-8f3b-ffb508517b67
- Local pid:
-
pubs:827817
- Deposit date:
-
2018-03-05
Terms of use
- Copyright holder:
- Binns et al
- Copyright date:
- 2018
- Notes:
- © 2018 Copyright held by the owner/author(s). Publication rights licensed to ACM. ISBN. 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]. This is the accepted manuscript version of the article. The final version is available online from the Association for Computing Machinery at: http://dx.doi.org/10.1145/3173574.3173967
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