Privacy-Preserving Synthetic Data for ML: The Role of Masked Language Models

Table of Links

1. Abstract & Introduction

2. Proposal

   1. Classification Target
   2. Masked Conditional Density Estimation (MaCoDE)

3. Theoretical Results

   1. With Missing Data

4. Experiments

5. Results

   1. Related Works
   2. Conclusions and Limitations
   3. References

6. A1 Proof of Theorem 1

   1. A2 Proof of Proposition 1
   2. A3 Dataset Descriptions

7. A4 Missing Mechanism

   1. A5 Experimental Settings for Reproduction

8. A6 Additional Experiments

9. A7 Detailed Experimental Results

A.1 Proof of Theorem 1

Proof. This proof is based on Theorem 6.11 of [50] and Theorem 1 of [29].

Thus, for every ϵ > 0,

(B) Furthermore, by the continuous mapping theorem and the algebra of the convergence in probability, for every ϵ > 0,

A.2 Proof of Proposition 1

A.3 Dataset Descriptions

Download links.

• abalone: https://archive.ics.uci.edu/dataset/1/abalone

• banknote: https://archive.ics.uci.edu/dataset/267/banknote+authentication

• breast: https://archive.ics.uci.edu/dataset/17/breast+cancer+wisconsin+diagnostic

• concrete: https://archive.ics.uci.edu/dataset/165/concrete+compressive+strength

• covertype: https://www.kaggle.com/datasets/uciml/forest-cover-type-dataset

• kings: https://www.kaggle.com/datasets/harlfoxem/housesalesprediction

• letter: https://archive.ics.uci.edu/dataset/59/letter+recognition

• loan: https://www.kaggle.com/datasets/teertha/personal-loan-modeling

• redwine: https://archive.ics.uci.edu/dataset/186/wine+quality

• whitewine: https://archive.ics.uci.edu/dataset/186/wine+quality