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Effectiveness and Users' Experience of Obfuscation as a Privacy-Enhancing Technology for Sharing Photos

Authors:

Nishant Vishwamitra,

Bart P. Knijnenburg,

Kelly CaineAuthors Info & Claims

Proceedings of the ACM on Human-Computer Interaction, Volume 1, Issue CSCW

Article No.: 67, Pages 1 - 24

https://doi.org/10.1145/3134702

Published: 06 December 2017 Publication History

Abstract

Current collaborative photo privacy protection solutions can be categorized into two approaches: controlling the recipient, which restricts certain viewers' access to the photo, and controlling the content, which protects all or part of the photo from being viewed. Focusing on the latter approach, we introduce privacy-enhancing obfuscations for photos and conduct an online experiment with 271 participants to evaluate their effectiveness against human recognition and how they affect the viewing experience. Results indicate the two most common obfuscations, blurring and pixelating, are ineffective. On the other hand, inpainting, which removes an object or person entirely, and avatar, which replaces content with a graphical representation are effective. From a viewer experience perspective, blurring, pixelating, inpainting, and avatar are preferable. Based on these results, we suggest inpainting and avatar may be useful as privacy-enhancing technologies for photos, because they are both effective at increasing privacy for elements of a photo and provide a good viewer experience.

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Cited By

Cruz ACosta RSantos LRabadão CMarto AGonçalves A(2025)Assessing User Perceptions and Preferences on Applying Obfuscation Techniques for Privacy Protection in Augmented RealityFuture Internet10.3390/fi1702005517:2(55)Online publication date: 25-Jan-2025
https://doi.org/10.3390/fi17020055
Laishram LShaheryar MLee JJung S(2025)Toward a Privacy-Preserving Face Recognition System: A Survey of Leakages and SolutionsACM Computing Surveys10.1145/367322457:6(1-38)Online publication date: 10-Feb-2025
https://dl.acm.org/doi/10.1145/3673224
Zhu HXiao YLi YShi GKrunz M(2025)SANSee: A Physical-Layer Semantic-Aware Networking Framework for Distributed Wireless SensingIEEE Transactions on Mobile Computing10.1109/TMC.2024.348327224:3(1636-1653)Online publication date: 1-Mar-2025
https://dl.acm.org/doi/10.1109/TMC.2024.3483272
Show More Cited By

Index Terms

Effectiveness and Users' Experience of Obfuscation as a Privacy-Enhancing Technology for Sharing Photos
1. Human-centered computing
  1. Collaborative and social computing
  2. Human computer interaction (HCI)
2. Security and privacy
  1. Human and societal aspects of security and privacy
    1. Privacy protections
    2. Usability in security and privacy

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cover image Proceedings of the ACM on Human-Computer Interaction

Proceedings of the ACM on Human-Computer Interaction Volume 1, Issue CSCW

November 2017

2095 pages

EISSN:2573-0142

DOI:10.1145/3171581

Editors:
Clifford Lampe
University of Michigan
,
Jeff Nichols
Google
,
Karrie Karahalios
University of Illinois Urbana-Champaign
,
Geraldine Fitzpatrick
Vienna University of Technology
,
Uichin Lee
KAIST
,
Andres Monroy-Hernandez
Snap Inc.
,
Wolfgang Stuerzlinger
Simon Fraser University

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Published: 06 December 2017

Published in PACMHCI Volume 1, Issue CSCW

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https://doi.org/10.3390/fi17020055
Laishram LShaheryar MLee JJung S(2025)Toward a Privacy-Preserving Face Recognition System: A Survey of Leakages and SolutionsACM Computing Surveys10.1145/367322457:6(1-38)Online publication date: 10-Feb-2025
https://dl.acm.org/doi/10.1145/3673224
Zhu HXiao YLi YShi GKrunz M(2025)SANSee: A Physical-Layer Semantic-Aware Networking Framework for Distributed Wireless SensingIEEE Transactions on Mobile Computing10.1109/TMC.2024.348327224:3(1636-1653)Online publication date: 1-Mar-2025
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