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Online Photo Privacy - Phase 1: Investigating Effective and Usable Obfuscation Methods

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Year: 2016 to Present | Clemson University | Collaborators: Dr. Kelly Caine, Dr. Bart Knijnenburg, Dr. Apu Kapadia, Dr. David Crandall, Dr. Hongxin Hu, Dr. Rakib Hasan, Dr. Nishant Vishwamitra | Skill: Experimental Design, Survey, Quantitative Analysis, Qualitative Analysis | Role: PhD Leader

Overview of My Doctoral Thesis

Through my doctoral research, I aim to provide a better online photo privacy protection strategy than existing approaches such as photo self-censorship and recipient control on Online Social Networks (OSNs). To inform the building for an effective and usable photo privacy protection system on OSNs, I gain understanding on the two parameters that influence photo privacy–photo content and recipient–through a series of studies and provide design guidelines. My research will benefit privacy researchers, online social network designers, policymakers, and computer vision researchers. I plan to answer the following questions.

Study and select promising obfuscation methods :

  • What are the effective and usable obfuscations?

  • As an extreme privacy protection scheme, is photo self-censorship prevalent? Can obfuscations combat it and encourage photo sharing?

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Understand what to obscure (content) and prevent from whom (recipient):

  • What is the sensitive content in photos to be obscured?

  • With different groups of recipients, what are users’ preferences of sharing different categories of sensitive content?

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To create a more collaborative and usable privacy protection mechanism, I need to have a better understanding of users’ current photo privacy protection behaviors:

  • Other than identified self-censorship and recipient control that OSNs provide, are there other privacy protection schemes that they often use?

Phase 1: Identify Effective and Usable Obfuscations

Study 1: Investigating Effective and Usable Obfuscations For Obscuring People

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, I 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. ​Two pilot studies are published in HFES and CV-COPS. The complete study is published and presented at CSCW 2018 by me.

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Research Method:
Controlled experiment; Online survey

Participants:
271 participants recruited on Amazon MTurk

Independent Variable:
14 obfuscation conditions (including as-is as the baseline condition)

Dependent Variables:

  • Obfuscation effectiveness: Identification success; Identification confidence

  • Users' experience: Photo satisfaction; Perceived Photo information sufficiency; Photo enjoyment; Perceived social presence; Obfuscation likability


Quantitative Data Analysis Method:

  • Logistic mixed-effects models

  • Linear mixed-effects models


For more details, please download our paper at https://dl.acm.org/citation.cfm?id=3134702
If you are not able to access the ACM Digital Library, please email me to request this paper.

Study 2: Avatar and Inpainting Are Robust When De-identifying Both Unfamiliar and Familiar People

In study 1, one limitation is that I only explored obfuscations’ effectiveness for de-identifying unfamiliar people (people in stimuli were unknown to the participants). I conducted an experiment where participants identified both familiar and unfamiliar people applied different obfuscation methods. I find that avatar and inpainting are robust to the increased likelihood of recognition associated with familiarity so they would be useful privacy enhancement tools on OSNs. I am preparing the manuscript for a journal publication.
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Research Method:
Controlled experiment; Online survey

Participants:
230 participants recruited on Amazon MTurk

Independent Variable:
7 obfuscation conditions by 2 familiarity levels (familiar vs. unfamiliar)

Dependent Variables:

  • Obfuscation effectiveness: Identification success; Identification confidence

  • Users' experience: Photo satisfaction; Perceived Photo information sufficiency; Photo enjoyment; Perceived social presence; Obfuscation likability


Quantitative Analysis Method:

  • Logistic mixed-effects models

  • Linear mixed-effects models

Study 3: Obscuring Scene Elements

My prior work only considers human as the most sensitive content to be obscured, however, various incidental information could also harm privacy, such as monitor or indoor scenes. I studied 11 filters applied to obfuscate 20 different objects and evaluated how effectively they protect privacy and preserve image quality for human viewers. This work has been published and was presented at CHI 2018.

Research Method:
Controlled experiment; Online survey

Sample:
570 participants recruited on Amazon MTurk

Independent Variable:

  • Scenes

  • Obfuscation methods


Dependent Variables:

  • Obfuscation effectiveness: Identification success; Identification confidence

  • Users' experience: Photo satisfaction; Perceived Photo information sufficiency; Visual aesthetics


Quantitative Analysis Method:

  • Fisher’s exact test with Bonferroni correction

  • Kruskal-Wallis test


For more details, please download our paper at https://dl.acm.org/citation.cfm?id=3173621
If you are not able to access the ACM Digital Library, please email me to request this paper.

Publications

Li, Y., Vishwamitra, N., Knijnenburg, B. P., Hu, H., & Caine, K. (2017). Effectiveness and Users’ Experience of Obfuscation as a Privacy-Enhancing Technology for Sharing Photos. In Proceedings of the ACM on Human-Computer Interaction 1, 2 (2017).

Hasan, R., Li, Y., Caine, K., Crandall, D. J., Hoyle, R., & Kapadia, A. (2019). Can Privacy Be Satisfying? On Improving Viewer Satisfaction for Privacy-Enhanced Photos Using Aesthetic Transforms. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems. ACM.

Li, Y.. (2018). Photo Privacy Protection on Online Social Networks. In Companion of the 2018 ACM Conference on Computer Supported Cooperative Work and Social Computing. ACM.

Li, Y., & Caine, K. (2018). Applying A Behavioral Theory of Privacy to Online Photo Sharing. In Proceedings of the ACM Conference on 2018 Networked Privacy Workshop at CSCW. ACM.

Li, Y., Vishwamitra, N., Hu, H., Knijnenburg, B. P., & Caine, K. (2017). Effectiveness and users’ experience of face blurring as a privacy protection for sharing photos via online social networks. In Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 61(1), 803-807. doi:10.1177/1541931213601694

Li, Y., Vishwamitra, N., Knijnenburg, B. P., Hu, H., & Caine, K. (2017, July). Blur vs. Block: Investigating the effectiveness of privacy-enhancing obfuscation for images. In Computer Vision and Pattern Recognition Workshops (CVPRW)  (pp. 1343-1351). IEEE.

Hasan, R., Hassan, E., Li, Y., Caine, K., Crandall, D., Hoyle, R., &  Kapadia, A. (2018). Viewer experience of obscuring scene elements in photos to enhance privacy. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. ACM, 47.

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