A Searchable Symmetric Encryption-Based Privacy Protection Scheme for Cloud-Assisted Mobile Crowdsourcing
Bibliographic record
Abstract
Mobile crowdsourcing (MC) has emerged as an efficient data collection and processing technique with the growing use of mobile devices. Mobile devices typically have numerous sensors to capture a variety of data types, including location information, speech, picture, and video data. Due to the lack of storage capacity and processing power of mobile devices, conducting in-depth analysis and computation of the data is impossible. Cloud-based MC is a viable solution to the issue of limited resources in data outsourcing. How to effectively represent and process encrypted heterogeneous data is an enormous challenge. To alleviate this matter, a unified encrypted-tensor model is proposed to represent heterogeneous data consisting of unstructured, semistructured, and structured data, which represents data in different formats and from various sources. Due to the heterogeneity of data, we devise the encrypted query index and implement the query scheme for structured, semistructured, and unstructured data by transforming heterogeneous data into a graph. We evaluated the search performance of our proposed scheme on real-world data sets. This article analyzes the aspects of time search efficiency, memory occupation, and approximation accuracy. Theoretical analysis and experimental results show that the searchable encryption method based on heterogeneous data proposed in this article can effectively represent and mine big data.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".