Toward Secure and Scalable Computation in Internet of Things Data Applications
Bibliographic record
Abstract
The ever-growing of Internet of Things (IoT) data and the new spectrum of data applications have stimulated IoT clients to outsource their data to cloud servers or datacenters. Apart from storage service, the IoT clients also desires the servers to execute functional operations per client's request. In this paper, we aim to design the secure mechanisms that allow the IoT clients to outsource their encrypted data to geographically distributed servers while supporting homomorphic computation functions. We leverage the distributed index framework to disassemble and spread data evenly across geographically distributed servers while employing the key-value store as the underlying structure for fast data retrieval. To support computing over encrypted data, we customize Shamir's secret sharing into our mechanisms to design a tunable scheme for the adaption of different IoT application scenarios. In particular, we design three tunable protocols to achieve the effective additive homomorphic computations while approaching efficiency in terms of servers utilization, computation, and storage overhead. Even the designs focus on the additive computation, we show that it can be readily extended to other types of homomorphic computations as well as verifying the correctness of stored data. Based on the proposed protocols, we design system prototypes, deploy them in Amazon Web services, and evaluate our construction experimentally. Through experimental results, we show that our designs can achieve the efficiency in various perspectives.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".