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Understanding the Impact of Information Representation on Willingness to Share Information

Published: 02 May 2019 Publication History

Abstract

Since the release of the first activity tracker, there has been a steady increase in the number of sensors embedded in wearable devices and with it in the amount and diversity of information that can be derived from these sensors. This development leads to novel privacy threats for users. In a web survey with 248 participants, we explored whether users' willingness to share private data is dependent on how the data is requested by an application. Specifically, requests can be formulated as access to sensor data or as access to information derived from the sensor data (e.g., accelerometer vs. sleep quality). We show that non-expert users lack an understanding of how the two representation levels relate to each other. The results suggest that the willingness to share sensor data over derived information is governed by whether the derived information has positive or negative connotations (e.g., training intensity vs. life expectancy). Using the results of the survey, we derive implications for supporting users in protecting their private data collected via wearable sensors.

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  • (2024)Wearable Activity Trackers: A Survey on Utility, Privacy, and SecurityACM Computing Surveys10.1145/364509156:7(1-40)Online publication date: 8-Feb-2024
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  • (2024)What You Experience is What We Collect: User Experience Based Fine-Grained Permissions for Everyday Augmented RealityProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642668(1-24)Online publication date: 11-May-2024
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cover image ACM Conferences
CHI '19: Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems
May 2019
9077 pages
ISBN:9781450359702
DOI:10.1145/3290605
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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Publication History

Published: 02 May 2019

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  1. privacy
  2. sensors
  3. sharing data

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CHI '19 Paper Acceptance Rate 703 of 2,958 submissions, 24%;
Overall Acceptance Rate 6,199 of 26,314 submissions, 24%

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Cited By

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  • (2024)Wearable Activity Trackers: A Survey on Utility, Privacy, and SecurityACM Computing Surveys10.1145/364509156:7(1-40)Online publication date: 8-Feb-2024
  • (2024)Sharing Frissons among Online Video Viewers: Exploring the Design of Affective Communication for Aesthetic ChillsProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642818(1-19)Online publication date: 11-May-2024
  • (2024)What You Experience is What We Collect: User Experience Based Fine-Grained Permissions for Everyday Augmented RealityProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642668(1-24)Online publication date: 11-May-2024
  • (2024)Designing a Data-Driven Survey System: Leveraging Participants' Online Data to Personalize SurveysProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642572(1-22)Online publication date: 11-May-2024
  • (2023)Privacy-Enhancing Technology and Everyday Augmented RealityProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/35695016:4(1-35)Online publication date: 11-Jan-2023
  • (2023)GA Approach to Optimize Training Client Set in Federated LearningIEEE Access10.1109/ACCESS.2023.330436811(85489-85500)Online publication date: 2023
  • (2023)Look Before You Leap! Perceptions and Attitudes Towards Inferences in Wearable Fitness TrackersHCI for Cybersecurity, Privacy and Trust10.1007/978-3-031-35822-7_27(399-418)Online publication date: 9-Jul-2023
  • (2022)Users Can Deduce Sensitive Locations Protected by Privacy Zones on Fitness Tracking AppsProceedings of the 2022 CHI Conference on Human Factors in Computing Systems10.1145/3491102.3502136(1-21)Online publication date: 29-Apr-2022
  • (2022)VIREO: Web-based Graphical Authoring of Vibrotactile Feedback for Interactions with Mobile and Wearable DevicesInternational Journal of Human–Computer Interaction10.1080/10447318.2022.210958439:20(4162-4180)Online publication date: 18-Aug-2022
  • (2021)Understanding users' knowledge about the privacy and security of browser extensionsProceedings of the Seventeenth USENIX Conference on Usable Privacy and Security10.5555/3563572.3563578(99-118)Online publication date: 9-Aug-2021
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