Can Privacy Be Satisfying?

Hasan, Rakibul; Li, Yifang; Hassan, Elham A.; Caine, Kelly; Crandall, David J.; Hoyle, Roberto; Kapadia, Apu

doi:10.1145/3290605.3300597

Cited by 29 publications

(7 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Hasan et al [48] developed a computer vision approach identifying bystanders in photos taken in public places to preserve their privacy. The potential limitations of item modification approaches are the possibility to infer data subject identity based on the features involved in the content (e.g., location, clothes, friends), delays in the content to be shared, requiring additional effort to increase the aesthetics of the photos [49,50,79], the possibility of false face detection, and possibly rising additional privacy issues by requiring the identification of all subjects within the content [137].…”

Section: Precautionary Mechanisms: Novel Solutions To Manage Mpcsmentioning

confidence: 99%

When Forcing Collaboration is the Most Sensible Choice

Cherubini

Niksirat

Boldi

et al. 2021

Proc. ACM Hum.-Comput. Interact.

View full text Add to dashboard Cite

Individuals share increasing amounts of personal multimedia data, exposing themselves (uploaders) as well as others (data subjects). Non-consensual sharing of multimedia data that depicts others raises so-called multiparty privacy conflicts (MPCs), which can have severe consequences. To limit the incidence of MPCs, a family of Precautionary mechanisms have recently been developed that force uploaders to collaborate with the other data subjects to prevent MPCs. However, there is still very little work on understanding how users perceive the precautionary mechanisms together with which ones they prefer and why. In addition, precautionary mechanisms have some limitations, e.g., they require linking content to the co-owners' identity. Therefore, we also explore alternatives to precautionary mechanisms and propose a new class of solutions-Dissuasive mechanisms-that aim at deterring the uploaders from sharing without consent. We then present a user-centric comparison of precautionary and dissuasive mechanisms, through a large-scale survey ( = 1792; a representative sample of adult Internet users). Our results showed that respondents prefer precautionary to dissuasive mechanisms. These enforce collaboration, provide more control to the data subjects, but also they reduce uploaders' uncertainty around what is considered appropriate for sharing. We learned that threatening legal consequences is the most desirable dissuasive mechanism, and that respondents prefer the mechanisms that threaten users with immediate consequences (compared with delayed consequences). Dissuasive mechanisms are in fact well received by frequent sharers and older users, while precautionary mechanisms are preferred by women and younger users. We discuss the implications for design, including considerations about side leakages, consent collection, and censorship. CCS Concepts: • Security and privacy → Usability in security and privacy; • Human-centered computing;

show abstract

Section: Precautionary Mechanisms: Novel Solutions To Manage Mpcsmentioning

confidence: 99%

When Forcing Collaboration is the Most Sensible Choice

Cherubini

Niksirat

Boldi

et al. 2021

Proc. ACM Hum.-Comput. Interact.

View full text Add to dashboard Cite

show abstract

“…For example, Anthony et al discuss how people routinely engage in behaviors to respect the privacy of others [71]. Other work seeks to make privacy 'fun' by encouraging owners of photos to apply stickers or redactions on bystanders [27], [50]. Our work on detecting bystanders should thus be seen as a necessary building block of larger automated frameworks that consider further action on photos.…”

Section: Limitations and Discussionmentioning

confidence: 99%

Automatically Detecting Bystanders in Photos to Reduce Privacy Risks

Hasan

Crandall

Fritz

et al. 2020

2020 IEEE Symposium on Security and Privacy (SP)

Self Cite

View full text Add to dashboard Cite

Photographs taken in public places often contain bystanders -people who are not the main subject of a photo. These photos, when shared online, can reach a large number of viewers and potentially undermine the bystanders' privacy. Furthermore, recent developments in computer vision and machine learning can be used by online platforms to identify and track individuals. To combat this problem, researchers have proposed technical solutions that require bystanders to be proactive and use specific devices or applications to broadcast their privacy policy and identifying information to locate them in an image.We explore the prospect of a different approach -identifying bystanders solely based on the visual information present in an image. Through an online user study, we catalog the rationale humans use to classify subjects and bystanders in an image, and systematically validate a set of intuitive concepts (such as intentionally posing for a photo) that can be used to automatically identify bystanders. Using image data, we infer those concepts and then use them to train several classifier models. We extensively evaluate the models and compare them with human raters. On our initial dataset, with a 10-fold cross validation, our best model achieves a mean detection accuracy of 93% for images when human raters have 100% agreement on the class label and 80% when the agreement is only 67%. We validate this model on a completely different dataset and achieve similar results, demonstrating that our model generalizes well.

show abstract

“…In our reviews, we checked if the authors declared consent collection before sharing photos in the paper (face-photo). Avoidance of violating photo privacy is possible through obfuscation (e.g., blurring or masking) [20], or by using photos that does not fully show the face of participants (e.g., photos with a VR headset on the eyes) [1].…”

Section: Privacymentioning

confidence: 99%

Criteria Definition for Research Ethics, Openness, and Transparency

Niksirat¹,

Goswami²,

Rao³

et al. 2023

Preprint

View full text Add to dashboard Cite

This document is supplementary material for the paper “Changes in Research Ethics, Openness, and Transparency in Empirical Studies between CHI 2017 and CHI 2022”; published at ACM CHI 2023. The document provides the rationale behind each criterion in detail with additional citations. We hope that knowing the rationale will better encourage practices related to research ethics,openness, and transparency.The original paper and paper and all supplementary materials are freely available at: https://doi.org/10.17605/osf.io/n25d6

show abstract

Can Privacy Be Satisfying?

Cited by 29 publications

References 34 publications

When Forcing Collaboration is the Most Sensible Choice

When Forcing Collaboration is the Most Sensible Choice

Automatically Detecting Bystanders in Photos to Reduce Privacy Risks

Criteria Definition for Research Ethics, Openness, and Transparency

Contact Info

Product

Resources

About