Data Security and Privacy Research Trends: LDA Topic Modeling
Bibliographic record
Abstract
Abstract With the rapid advancement of big data technologies, the need for robust data security and privacy measures has intensified. Big data technologies have revolutionized the collection and analysis of a vast volume of research literature, offering unparalleled avenues for scholarly inquiry. Identifying prevalent research topics and discerning developmental trends is paramount, especially when grounded in an expansive literature base. This study examined abstracts and author keywords from 4,311 pertinent articles published between 1980 and 2023, sourced from the Web of Science core collection. The content of abstracts and author keywords underwent LDA theme modeling analysis. Consequently, five predominant research topics emerged: security and privacy measures for mobile applications, encryption protocols tailored for image security, privacy considerations in healthcare, intricate access control combined with security in cloud computing through attribute encryption, and ensuring security and information integrity for big data within the Internet of Things framework. The LDA model proficiently pinpoints these salient topics, assisting researchers in comprehending the current state of the domain and guiding potential future research trajectories.
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 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 | 0.017 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.023 | 0.030 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".