Use of Bloom Filters to convey identity of surveillance targets
Bibliographic record
Abstract
Local data offload performed at a small cell is not commercially deployed as a result of lawful interception issues. Previous work has described a solution to address these issues where a local node performs the same lawful interception functions as is done within the mobile core network for traffic that is offloaded at that local node. However, one issue with this solution is how to transfer the identities of those under surveillance to the local node. Passing the actual identities of targets of surveillance could comprise the required secretive nature of the surveillance. Another issue with the original solution is that by examining the traffic through a small cell, an unauthorized person could determine that a user's traffic is not being locally offloaded; thereby perhaps indicating that person is a target of surveillance. In this paper, we propose the use of Bloom Filters to convey the identities of those subscribers who are the target of surveillance. The solution is presented and an analysis is included to demonstrate the benefits of the solution. This paper demonstrates that the use of the Bloom Filter hides the identities of the subscribers who are under surveillance. As well, the paper demonstrates that the false positives that occur with a Bloom Filter are actually a benefit from a perspective of obfuscating who is the actual target of surveillance.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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; 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".