PACAS: Privacy-Aware, Data Cleaning-as-a-Service
Bibliographic record
Abstract
Data cleaning consumes up to 80% of the data analysis pipeline. This is a significant overhead for organizations where data cleaning is still a manually driven process requiring domain expertise. Recent advances have fueled a new computing paradigm called Database-as-a-Service, where data management tasks are outsourced to large service providers. We propose a new Data Cleaning-as-a-Service model that allows a client to interact with a data cleaning provider who hosts curated, and sensitive data. We present PACAS: a Privacy-Aware data Cleaning-As-a-Service framework that facilitates communication between the client and the service provider via a data pricing scheme where clients issue queries, and the service provider returns clean answers for a price while protecting her data. We propose a practical privacy model in such interactive settings called (X,Y,L)-anonymity that extends existing data publishing techniques to consider the data semantics while protecting sensitive values. Our evaluation over real data shows that PACAS effectively safeguards semantically related sensitive values, and provides improved accuracy over existing privacy-aware cleaning techniques.
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 | 0.010 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.005 |
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".