Privacy-Preserving Photo Sharing on Online Social Networks: A Review
Bibliographic record
Abstract
Online social networking applications and their services have become an essential part of the human lifestyle.These social services help online users in different ways, such as through social visibility, content sharing, communication, promotions, etc.In the same way, the services of online social networks (OSNs) pose privacy risks for their users.The service of photo sharing in OSNs, in particular, causes leakage of online social users' personal information.It's critical to comprehend previous studies that looked at privacypreserving photo sharing and whether users have their privacy protected when sharing photos.The goal of this review is to bridge the gap between the growing demand for image sharing via OSNs and individualized privacy requirements.This effort presents a comprehensive analysis of "privacy-preserving" technologies that specifically address contemporary privacy concerns associated with sharing images on online social networks (OSNs).This study presents a comprehensive analysis framework that focuses on the complete lifecycle of image sharing on online social networks (OSNs).This work presented a review framework called Privacy-Preserving Photo Sharing (PPPS) by categorizing previous works into three stages related to online secure photo sharing.Preprocessing, privacy settings, and photo publishing are the stages used in this survey's design to secure photo sharing.This framework aims to tackle the many privacy challenges and propose appropriate solutions in this multidisciplinary domain.During each phase, we analyse common user behaviours connected to sharing, the privacy concerns that arise from those behaviours, and evaluate representative solutions that are proposed by previous works.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".