Exploring the Tight Asymptotic Bounds of the Trade-off Between Query Anonymity & Communication Cost in Wireless Sensor Network
Bibliographic record
Abstract
We address query-anonymity in the context of wireless sensor networks. Query-anonymity is the property that the destination of a client’s query is indistinguishable from other potential destinations. Prior work has established that this is an important issue, and has also pointed out that there appears to be a natural trade-off between query-anonymity and communication-cost. We explore what we call the limits of this trade-off: what is the communication-cost that is sufficient to achieve a certain query-anonymity, and what is the communication-cost that we must necessarily incur to achieve a certain query-anonymity? We adopt an unconditional notion of query-anonymity that we argue has intuitive appeal. We then establish the limits of the trade-off. In particular, we show that in wireless sensor \nnetworks which are source-routed, the necessary and sufficient communication-cost for query-anonymity asymptotically smaller than the diameter of the network d is a function of d only, and the necessary and sufficient communication-cost for query-anonymity larger than d is a function of the desired query-anonymity only. Our result applies to any network topology that is an arbitrary connected undirected graph. We validate our analytical insights empirically, via simulations. In summary, our work establishes sound and interesting theoretical results for query-anonymity in wireless sensor networks, and validates them empirically.
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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 | 0.013 | 0.123 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.005 | 0.016 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".