🛠️ Synthetic Data Hub

What is a Synthetic data: Artificially generated data rather than produced by real-world events. Typically created using algorithms, synthetic data can be deployed to validate mathematical models and to train machine learning models.

Data generated by a computer simulation can be seen as synthetic data. This encompasses most applications of physical modeling, such as music synthesizers or flight simulators. The output of such systems approximates the real thing, but is fully algorithmically generated.

Synthetic data is used in a variety of fields as a filter for information that would otherwise compromise the confidentiality of particular aspects of the data. In many sensitive applications, datasets theoretically exist but cannot be released to the general public; synthetic data sidesteps the privacy issues that arise from using real consumer information without permission or compensation.

Usefulness

Synthetic data is generated to meet specific needs or certain conditions that may not be found in the original, real data. One of the hurdles in applying up-to-date machine learning approaches for complex scientific tasks is the scarcity of labeled data, a gap effectively bridged by the use of synthetic data, which closely replicates real experimental data. This can be useful when designing many systems, from simulations based on theoretical value, to database processors, etc. This helps detect and solve unexpected issues such as information processing limitations. Synthetic data are often generated to represent the authentic data and allows a baseline to be set. Another benefit of synthetic data is to protect the privacy and confidentiality of authentic data, while still allowing for use in testing systems.

A science article’s abstract, quoted below, describes software that generates synthetic data for testing fraud detection systems. “This enables us to create realistic behavior profiles for users and attackers. The data is used to train the fraud detection system itself, thus creating the necessary adaptation of the system to a specific environment.” In defense and military contexts, synthetic data is seen as a potentially valuable tool to develop and improve complex AI systems, particularly in contexts where high-quality real-world data is scarce. At the same time, synthetic data together with the testing approach can give the ability to model real-world scenarios.

Github Repository Description

A collection of synthetic data designed for testing, validating, and ensuring the effectiveness of Data Loss Prevention (DLP) and Data Security Posture Management (DSPM) solutions. This repository includes sample datasets and scripts to generate new data, covering various categories such as Personal Identifiable Information (PII), Human Resources (HR), Payment Card Industry (PCI) compliant data, Protected Health Information (PHI), and others.

Features

  • Comprehensive Datasets: Provides synthetic data across multiple categories, including PII, HR, PCI, and PHI.
  • Customizable Data Generation: Offers Python scripts to generate data in various formats (e.g., JSON, CSV, Excel, Word, PDF, or TXT) and locales (e.g., en_US, fr_FR), allowing tailored testing scenarios.
  • Enhances Security Testing: Facilitates the testing and validation of DLP and DSPM solutions without compromising real data.

Getting Started

Clone this repository to your local machine using Git or download the ZIP file.