On the use of wavelet transform for privacy preserving data mining
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
Data mining is the process of automatically searching large amount of data to extract useful information and patterns using tools such as classification, association and rule mining. Data mining often involves data that contains private information such as healthcare or financial records and there has been growing concern about the chance of misusing the personal information extracted from such data. In particular, the increasing ability to trace and collect large amount of data with the use of current technology has led to an interest in the development of data mining algorithms which preserve user privacy. Data perturbation is one of the well known techniques for privacy preserving data mining. In this paper, we investigate the use of the Discrete Wavelet Transform (DWT) with truncation for data perturbation. Our experimental results show that the proposed method is effective in concealing the sensitive information while preserving the performance of data mining techniques after the data distortion.
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 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.031 | 0.023 |
| 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