Privacy Risk in Graph Stream Publishing for Social Network Data
Bibliographic record
Abstract
To understand how social networks evolve over time, graphs representing the networks need to be published periodically or on-demand. The identity of the participants (nodes) must be anonymized to protect the privacy of the individuals and their relationships (edges) to the other members in the social network. We identify a new form of privacy attack, which we name the degree-trail attack. This attack re-identifies the nodes belonging to a target participant from a sequence of published graphs by comparing the degree of the nodes in the published graphs with the degree evolution of a target. The power of this attack is that the adversary can actively influence the degree of the target individual by interacting with the social network. We show that the adversary can succeed with a high probability even if published graphs are anonymized by strongest known privacy preserving techniques in the literature. Moreover, this success does not depend on the distinctiveness of the target nodes nor require the adversary to behave differently from a normal participant. One of our contributions is a formal method to assess the privacy risk of this type of attacks and empirically study the severity on real social network data.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.006 |
| Open science | 0.084 | 0.195 |
| Research integrity | 0.000 | 0.000 |
| 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; both teacher heads agree on what is shown here.
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".