Image-Centric Social Discovery Using Neural Network under Anonymity Constraint
Bibliographic record
Abstract
Image sharing is one of the most attractive features facilitated by different social media sites such as Facebook, Flickr, Pinterest, and Instagram. People frequently use these social media sites to express various aspects of their life with peers they are connected through these sites. The service providers of these sites sometimes use the image features for social discovery such as friend recommendation, group or community recommendation, etc. As images are rich in content and more expressive, it also reveals much sensitive information about a user and impedes their privacy. Due to storage constraints, many popular social media sites prefer to outsource their data to the cloud server. However, if the cloud server gets compromised, then an adversary can use these sensitive images for malicious purposes. In this paper, we propose a privacy-preserving image-centric social discovery framework using the neural network and efficient anonymization scheme based on optimum feature selection. Experimental results show that our proposed approach provides better accuracy than existing method as well as is scalable for big datasets.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".