‘Interdependent’ privacy violations occur when users share private photos and information about other people in social media without permission. This research investigated user characteristics associated with interdependent privacy perceptions, by asking social media users to rate photo-based memes depicting strangers on the degree to which they were too private to share. Users also completed questionnaires measuring social media usage and personality. Separate groups rated the memes on shareability, valence, and entertainment value. Users were less likely to share memes that were rated as private, except when the meme was entertaining or when users exhibited dark triad characteristics. Users with dark triad characteristics demonstrated a heightened awareness of interdependent privacy and increased sharing of others’ photos. A model is introduced that highlights user types and characteristics that correspond to different privacy preferences: privacy preservers, ignorers, and violators. We discuss how interventions to support interdependent privacy must effectively influence diverse users.
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Learning and Preserving Relationship Privacy in Photo Sharing
In recent years, Online Social Networks (OSN) have become popular content-sharing environments. With the emergence of smartphones with high-quality cameras, people like to share photos of their life moments on OSNs. The photos, however, often contain private information that people do not intend to share with others (e.g., their sensitive relationship). Solely relying on OSN users to manually process photos to protect their relationship can be tedious and error-prone. Therefore, we designed a system to automatically discover sensitive relations in a photo to be shared online and preserve the relations by face blocking techniques. We first used the Decision Tree model to learn sensitive relations from the photos labeled private or public by OSN users. Then we defined a face blocking problem and developed a linear programming model to optimize the tradeoff between preserving relationship privacy and maintaining the photo utility. In this paper, we generated synthetic data and used it to evaluate our system performance in terms of privacy protection and photo utility loss.
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- Award ID(s):
- 1712496
- NSF-PAR ID:
- 10438705
- Date Published:
- Journal Name:
- EEE/ACM International Conference on Big Data Computing, Applications and Technologies
- Page Range / eLocation ID:
- 170 to 173
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Today, face editing is widely used to refine/alter photos in both professional and recreational settings. Yet it is also used to modify (and repost) existing online photos for cyberbullying. Our work considers an important open question: 'How can we support the collaborative use of face editing on social platforms while protecting against unacceptable edits and reposts by others?' This is challenging because, as our user study shows, users vary widely in their definition of what edits are (un)acceptable. Any global filter policy deployed by social platforms is unlikely to address the needs of all users, but hinders social interactions enabled by photo editing. Instead, we argue that face edit protection policies should be implemented by social platforms based on individual user preferences. When posting an original photo online, a user can choose to specify the types of face edits (dis)allowed on the photo. Social platforms use these per-photo edit policies to moderate future photo uploads, i.e., edited photos containing modifications that violate the original photo's policy are either blocked or shelved for user approval. Realizing this personalized protection, however, faces two immediate challenges: (1) how to accurately recognize specific modifications, if any, contained in a photo; and (2) how to associate an edited photo with its original photo (and thus the edit policy). We show that these challenges can be addressed by combining highly efficient hashing based image search and scalable semantic image comparison, and build a prototype protector (Alethia) covering nine edit types. Evaluations using IRB-approved user studies and data-driven experiments (on 839K face photos) show that Alethia accurately recognizes edited photos that violate user policies and induces a feeling of protection to study participants. This demonstrates the initial feasibility of personalized face edit protection. We also discuss current limitations and future directions to push the concept forward.
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/BDCAT56447.2022.00029
