Combinatorial techniques for key distribution and information storage
Bibliographic record
Abstract
We address four security problems in electronic information systems. First, we analyze tree-based broadcast encryption schemes. The challenge of broadcast encryption lies in minimizing storage and the number of encryptions while maintaining system security. Tree-based key distribution schemes are the best known broadcast encryption schemes. We introduce generating functions for this family of schemes, which lead to analysis of the mean number of encryptions. We also introduce approximations that are easy to calculate and significantly more accurate than previously known estimations. Second, we propose communication-efficient schemes for wireless sensor networks. A secure communication link is established between sensors that hold common keys, which are discovered by each node broadcasting the key identifiers of its key ring. We make modifications to existing schemes so that common keys can be discovered efficiently. Our modifications do not weaken network connectivity or system resiliency against key compromise. We also introduce families of new deterministic key distribution schemes with low-communication overhead. Third, we introduce decoding algorithms for subset batch codes. The use of subset codes can attain essentially optimal system parameters for private information retrieval protocols and make them practical. Our decoding algorithm gives the optimal recoverability parameter for certain types of multisets. While a general decoding algorithm appears very difficult to develop, our decoding algorithms suggest directions for further development of practical codes for private information retrieval. Lastly, we introduce an extended Steiner system ES( t, k, v), a collection of k-multisets (called blocks) of a v-set such that every t-multiset belongs to exactly one block. It provides a natural solution to the problem of retrieving a multiset of items by accessing only one server. For such designs, the number of blocks is not unique. An extended triple system, with t = 2 and k = 3, was previously known. We show constructions of ES(3, 4, v) for infinitely many values of v with minimum number of blocks. We also present a simple construction of ES(2, q + 2, q 2 + q + 1) for a prime power q.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".