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Who's viewed you?: the impact of feedback in a mobile location-sharing application

Published: 04 April 2009 Publication History

Abstract

Feedback is viewed as an essential element of ubiquitous computing systems in the HCI literature for helping people manage their privacy. However, the success of online social networks and existing commercial systems for mobile location sharing which do not incorporate feedback would seem to call the importance of feedback into question. We investigated this issue in the context of a mobile location sharing system. Specifically, we report on the findings of a field de-ployment of Locyoution, a mobile location sharing system. In our study of 56 users, one group was given feedback in the form of a history of location requests, and a second group was given no feedback at all. Our major contribution has been to show that feedback is an important contributing factor towards improving user comfort levels and allaying privacy concerns. Participants' privacy concerns were reduced after using the mobile location sharing system. Additionally,our study suggests that peer opinion and technical savviness contribute most to whether or not participants thought they would continue to use a mobile location technology.

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cover image ACM Conferences
CHI '09: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
April 2009
2426 pages
ISBN:9781605582467
DOI:10.1145/1518701
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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Published: 04 April 2009

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Author Tags

  1. context-awareness
  2. information disclosure
  3. mobile location sharing technology
  4. mobile social
  5. privacy

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CHI '09 Paper Acceptance Rate 277 of 1,130 submissions, 25%;
Overall Acceptance Rate 6,199 of 26,314 submissions, 24%

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  • (2023)How to Influence Privacy Behavior Using Cognitive Theory and Respective Determinant FactorsJournal of Cybersecurity and Privacy10.3390/jcp30300203:3(396-415)Online publication date: 17-Jul-2023
  • (2022)Detecting and Rectifying the Non-Malicious Insider Threat in a Healthcare SettingInternational Journal of Systems and Software Security and Protection10.4018/IJSSSP.31576613:1(1-20)Online publication date: 30-Dec-2022
  • (2022)Context Adaptive Personalized Privacy for Location-based SystemsAdjunct Proceedings of the 30th ACM Conference on User Modeling, Adaptation and Personalization10.1145/3511047.3537677(101-108)Online publication date: 4-Jul-2022
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  • (2022)An Exploratory Analysis of Interface Features Influencing Mobile Location Data DisclosureInternational Journal of Human–Computer Interaction10.1080/10447318.2022.210442839:19(3758-3773)Online publication date: 8-Aug-2022
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  • (2021)LYLO – Exploring Disclosed Configurations for Inter-Personal Location SharingExtended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems10.1145/3411763.3451652(1-6)Online publication date: 8-May-2021
  • (2020)Traces of mobilityTelematics and Informatics10.1016/j.tele.2020.10136649:COnline publication date: 1-Jul-2020
  • (2019)AncileProceedings of the 18th ACM Workshop on Privacy in the Electronic Society10.1145/3338498.3358642(111-124)Online publication date: 11-Nov-2019
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