Skip to content

Identity Disclosure Risk in a Fully Synthetic Dataset

Privacy preserving synthetic data

In today's digital age, data has become an integral part of every organization's operations. Companies gather and analyze vast amounts of data to make informed decisions and gain insights into their customers' behavior and preferences. However, the collection and processing of sensitive and personal information have raised concerns about privacy and security. This is where synthetic data comes in as a solution for data-sharing initiatives while ensuring privacy.

Synthetic data is artificially generated data that mimics real data patterns without duplicating the original dataset. It is generated through machine learning techniques that learn the statistical information from real data, enabling the creation of new, artificial data that is not linked to actual individuals or events. This data can be used in place of sensitive data for testing and development purposes, allowing for more secure and responsible data sharing.

In this use case, YData Fabric showcases the use of synthetic data for data-sharing initiatives and how to assess the risk of disclosure. YData Fabric's synthesizers use state-of-the-art machine learning techniques to generate synthetic data that reproduces the patterns and characteristics of the original data without compromising privacy.



Synthetic data generation with Gaussian Mixture Models

Photo by Roman Synkevych on Unsplash   A probabilistic approach to fast synthetic data generation with ydata-synthetic To find synthetic data generation within the same sentence as Gaussian Mixture Models (GMMs) sounds odd, but it makes a...

Read More

Democratized access for large transactional datasets

The value of data and the adoption of data-driven strategies have proven valuable for many organizations, particularly the financial services sector, with cases ranging from fraud detection to improved credit scoring. Nevertheless, privacy...

Read More

Detecting the failures that are worth preventing

The adoption of a data-driven strategy for Predictive Maintenance empowers energy and utility industries. A successful Predictive Maintenance implementation can reduce maintenance costs by 25–35%, eliminate downtimes by 70–75%, reduce...

Read More