license:apache-2.0 size_categories:1K<n<10K format:json modality:text library:datasets library:pandas library:mlcroissant library:polars region:us
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license: apache-2.0
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# Synthetic Dataset Data Card

This document provides an overview of the synthetic dataset generated for testing redaction and anonymization pipelines. It outlines the data generation process, the variety in data formats, ethical considerations, and the impact of complex invalid formats on model quality.

## Overview

The synthetic dataset is created using a suite of generators that produce both valid and intentionally invalid formats for sensitive data such as names, card numbers, account numbers, social security numbers, and more. The generated data is then embedded into coherent sentences by leveraging an LLM (Llama/Granite), which forces adherence to the complexity of the given data.

## Data Generation Process

- **Sensitive Data Generation First:**  
  Sensitive fields are generated in isolation to ensure an exact format. These values are later supplied to the LLM to create sentences that naturally integrate the data without altering the original values.

- **Inclusion of Noise & Invalid Formats:**  
  Noise is added deliberately to simulate real-world data imperfections. This includes:
  - Random variations in letter case.
  - Extra or missing spaces.
  - Insertion of special characters.
  - Deliberately malformed formats and ambiguous separators.

- **Diverse Data Types:**  
  The generators create a variety of sensitive fields, including people names, card numbers, account numbers, SSNs, government IDs, passwords, tax IDs, phone numbers, addresses, email addresses, IPs, passports, and driver licenses.

## Impact on Model Quality

- **Enhancement through Complexity:**  
  By incorporating both legitimate and non-legitimate (noisy or malformed) data formats, the dataset forces models to learn robust representations. This complexity helps prevent overfitting and improves the overall generalizability of models on data redaction tasks.

- **Realistic Simulation:**  
  The introduction of errors and noise mirrors real-world data issues, ensuring that redaction and anonymization pipelines are rigorously tested against unpredictable input formats.

## Ethical Considerations

- **Synthetic Nature of Data:**  
  Since this dataset is entirely synthetic, it does not contain personally identifying information from real individuals, reducing privacy concerns and ensuring compliance with ethical standards.

- **Bias and Fairness Awareness:**  
  Despite its synthetic nature, careful attention is paid to avoid introducing biases in data generation. Diverse data types and noise ensure that the models trained using this dataset are less prone to biases that may emerge from overly sanitized or uniform datasets.

- **Responsible Use:**  
  The dataset is intended solely for testing, research, and improving data anonymization pipelines. Users must ensure that any models trained on this data are evaluated for ethical implications and performance on real-world data scenarios.

## Conclusion

This dataset is a crucial tool in developing robust redaction and anonymization systems. The combination of valid and intentionally distorted data formats challenges models to perform under variable conditions, ultimately leading to more resilient and ethical solutions in sensitive data handling.

For further questions or contributions related to this data card, please refer to the project documentation or contact the repository maintainers.