PrivacyCamera: Cooperative Privacy-Aware Photographing with Mobile Phones

Li, Ang; Li, Qinghua; Gao, Wei

doi:10.1109/sahcn.2016.7733008

Cited by 18 publications

(15 citation statements)

References 22 publications

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“…Face blurring is as likeable and only slightly less attractive in terms of other elements of users' experience and does provide some amount of privacy enhancement. Given this, it make sense that face blurring is a commonly used filter in existing literature (Ilia et al, 2015;A. Li et al, 2016) and in practice (e.g., in Google's street view, people's faces are blurred).…”

Section: Discussionmentioning

confidence: 99%

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Effectiveness and Users’ Experience of Face Blurring as a Privacy Protection for Sharing Photos via Online Social Networks

Vishwamitra

et al. 2017

Proceedings of the Human Factors and Ergonomics Society Annual

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Photo sharing on online social networks (OSNs) can cause privacy issues. Face blurring is one strategy to increase privacy while still allowing users to share photos. To explore the potential blurring has as a privacy-enhancing technology for OSN photos, we conducted an online experiment with 47 participants to evaluate the effectiveness of face blurring compared to the original photo (as-is), and users’ experience (satisfaction, information sufficiency, enjoyment, social presence, and filter likeability). Users’ experience ratings for face blurring were positive, indicating blurring may be an acceptable way to modify photos from the users’ perspective. However, from a privacy-enhancement perspective, while face blurring may be useful in some situations, such as those where the person in the photo is unknown to the viewer, in other cases, such as in an OSN where the person in the image is known to the viewer, face blurring does not provide privacy protection.

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Section: Discussionmentioning

confidence: 99%

“…In this approach, part of the photo is hidden, for example by blurring, pixelating or distorting a person's face to avoid identification. Blurring is the most commonly used and widely studied approach to controlling photo content disclosure (e.g., Ilia, Polakis, Athanasopoulos, Maggi, & Ioannidis, 2015;Li, Li, & Gao, 2016).…”

Section: Introductionmentioning

confidence: 99%

Effectiveness and Users’ Experience of Face Blurring as a Privacy Protection for Sharing Photos via Online Social Networks

Vishwamitra

et al. 2017

Proceedings of the Human Factors and Ergonomics Society Annual

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“…ObscuraCam [13] Respectful cameras [21] Invisible Light Beacons [23] Negative face blurring [24] Device-based-collaborative I-pic [14] PrivacyCamera [20] Do Not Capture [22] IoT 2020, 1 203…”

Section: Bystander-basedmentioning

confidence: 99%

“…Device-based obfuscation: In this group, third-party devices which are not owned by the bystander perform blurring or add noise (in the signal processing sense) to the image captured from the bystander to hide his/her identity. Depending on how the software at the capturing device performs the blurring, solutions in this category can be further classified into default obfuscation (any face in the image will be blurred) [19], selective obfuscation (third-party device users select who to obfuscate in the image) [20], or collaborative obfuscation (third-party and bystander's device collaborate via wireless protocols [47] to allow a face to be blurred) [21]. A drawback of device-based obfuscation method is that a bystander must trust a device that he/she does not control to protect his/her privacy.…”

mentioning

confidence: 99%

A User Study of a Wearable System to Enhance Bystanders’ Facial Privacy

Pérez

Zeadally

Griffith

et al. 2020

IoT

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The privacy of users and information are becoming increasingly important with the growth and pervasive use of mobile devices such as wearables, mobile phones, drones, and Internet of Things (IoT) devices. Today many of these mobile devices are equipped with cameras which enable users to take pictures and record videos anytime they need to do so. In many such cases, bystanders’ privacy is not a concern, and as a result, audio and video of bystanders are often captured without their consent. We present results from a user study in which 21 participants were asked to use a wearable system called FacePET developed to enhance bystanders’ facial privacy by providing a way for bystanders to protect their own privacy rather than relying on external systems for protection. While past works in the literature focused on privacy perceptions of bystanders when photographed in public/shared spaces, there has not been research with a focus on user perceptions of bystander-based wearable devices to enhance privacy. Thus, in this work, we focus on user perceptions of the FacePET device and/or similar wearables to enhance bystanders’ facial privacy. In our study, we found that 16 participants would use FacePET or similar devices to enhance their facial privacy, and 17 participants agreed that if smart glasses had features to conceal users’ identities, it would allow them to become more popular.

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“…Templeman et al [36] implemented a system PlaceAvoider to protect visual privacy by identifying sensitive places in video streams. Li et al [37], [38] proposed two systems for protecting bystanders' privacy in photo taking. Tan et al [11] designed an access control scheme to protect private photos on mobile phones, but it depends on pre-specified target faces on mobile phones and can only provide limited protection.…”

Section: Related Workmentioning

confidence: 99%

PhotoSafer: Content-Based and Context-Aware Private Photo Protection for Smartphones

Darling

2018

2018 IEEE Symposium on Privacy-Aware Computing (PAC)

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Nowadays many people store photos in smartphones. Many of the photos contain sensitive, private information, such as a photocopy of driver's license and credit card. An arising privacy concern is with the unauthorized accesses to such private photos by installed apps. Coarse-grained access control systems such as the Android permission system offer all-or-nothing access to photos stored on smartphones, and users are unaware of the exact behavior of installed apps. Our analysis finds that 82% of the top 200 free apps in a popular Android app store have complete access to stored photos and network on a user's smartphone, which indicates possible private photo leakage. In addition, our user survey reveals that 87.5% of the 112 respondents are not aware that certain apps can access their photos without informing users, and all the respondents believe that the stored photos on their smartphones contain different types of private information. Hence, we propose PhotoSafer, a content-based, context-aware private photo protection system for Android phones. PhotoSafer can detect private photos based on photo content with a welltrained deep convolutional neural network, and control access to photos based on system status (e.g., screen locked or not) and app-running status (e.g., app in the background). Evaluations demonstrate that PhotoSafer can accurately identify private photos in real time. The efficacy and efficiency of the implemented prototype system show the potential for practical use.

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PrivacyCamera: Cooperative Privacy-Aware Photographing with Mobile Phones

Cited by 18 publications

References 22 publications

Effectiveness and Users’ Experience of Face Blurring as a Privacy Protection for Sharing Photos via Online Social Networks

Effectiveness and Users’ Experience of Face Blurring as a Privacy Protection for Sharing Photos via Online Social Networks

A User Study of a Wearable System to Enhance Bystanders’ Facial Privacy

PhotoSafer: Content-Based and Context-Aware Private Photo Protection for Smartphones

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