Will you be my friend? privacy implications of accepting friendships in online social networks
Bibliographic record
Abstract
Online social networks (OSNs) have become extremely popular in recent years. Users actively interact in these networks and share large amounts of personal information. This has led to emergence of a treasure trove of data for many entities, from marketers and spammers to employers and intelligence agencies, which has become a serious privacy concern. Previous works have addressed many aspects about privacy in OSNs such as characterizing potential privacy leakage [14], possible ways for inferring sensitive private information [9], [18], and appropriateness of default privacy settings [11]. In contrast, we focus on the entity who plays the main role in guarding privacy: the user. By sending out friend requests to unknown users in one of the largest OSNs, we provide evidence that a considerable portion of OSN users are willing to let a stranger, possibly an adversary, into their social network, thus granting her access to the users' personal information and to some extent to those of their friends. We study several factors that might foster such behavior, and measure the amount of information that will consequently become accessible. We find that for more than 95% of the users who accept our friend requests, we gained access to personal information that would not otherwise be accessible. We also show that the majority of the users who accept the requests have indeed changed their default privacy settings to restrict access to some parts of their personal information to their friends while making them publicly inaccessible.
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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 | 0.004 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".