Providing and evaluating a model for big data anonymization streams by using in-memory processing
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Extracting valuable information from vast sources of social networks while protecting confidentiality and preventing data disclosure is a significant challenge in big data environments. Traditional anonymization methods often fall short in handling the volume, variety, and velocity of big data, leading to high data loss and inefficiency. This article addresses these challenges by proposing a novel anonymization method based on K-means clustering within the Spark framework, leveraging its in-memory processing capabilities. Our model uses K-means clustering to determine optimal cluster heads, significantly reducing data loss and identity disclosure risks. By utilizing Spark's RDD abilities and the MLlib component, our method achieves faster processing times compared to traditional methods that rely on non-in-memory big data tools. Performance evaluation demonstrates that at k = 9, the cost factor is minimized to 0.20, indicating the efficiency and effectiveness of our approach. The proposed method not only enhances processing speed but also ensures minimal data loss, making it suitable for real-time anonymization of big data streams. This work provides a balanced solution that addresses the critical need for high-speed data anonymization while maintaining data privacy and utility.
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.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.007 |
| Open science | 0.003 | 0.010 |
| 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 it