FIDES

Privatized Data Synthesizer
Secure Data for Scalable Replication

FIDES

Privatized Synthetic Data Engine
Secure Data for Scalable Replication

Fides protects sensitive information without limiting what you can do with your data, from sharing it for data mining to monetizing it. This helps you to make the most of your data without compromising on compliance with regulations like GDPR and CCPA.

Your Journey Onboarding Fides

Fides constructs a deep generative model to replicate your data at the same granularity for multiple purposes. You can save your model and keep generating data when you need it, assuring privacy of datasets.

Value Propositions

Real-world data
ready

Accommodates tabular micro-data with both numerical and/or categorical fields that are most common in enterprise use cases

Mathematically
guaranteed privacy

Ensures differential privacy by withholding sensitive information when exposing a dataset

Model-building ready
datasets

Generates data at the same granularity as what was fed in with built-in optimizations

Secure integration
at scale

Connects to multiple sources and scales to massive datasets on the Cloud without compromising global privacy regulations

Real-world data
ready

Accommodates tabular micro-data with both numerical and/or categorical fields that are most common in enterprise use cases

Mathematically guaranteed privacy

Ensures differential privacy by withholding sensitive information when exposing a dataset

Model-building ready datasets

Generates data at the same granularity as what was fed in with built-in optimizations

Secure integration at scale

Connects to multiple sources and scales to massive datasets on the Cloud without compromising global privacy regulations

Success Stories

Financial Services Firm Achieves 98% Similarity Between Original and Synthetic Data thus Enabling Unrestricted Use

A financial services company was able to preserve all its data properties and reproduce synthetic data with a 98% rate of accuracy, enabling more insightful decision making. With the use of Fides, the company was able to easily allow their strategists to determine a criteria for loan approval by accessing core business data without compromising on data privacy.

Fides utilized deep generative models to enable data sharing by generating synthetic data with differential privacy, ensuring individual privacy. Datasets generated in this way were optimized for modeling with the same performance characteristics as those built on real data, so models built on these cloned datasets were able to create as much utility for applications as those built on real data.

Healthcare Company Reports 99.14% fidelity in Synthetic Data Used for Medical Research

A healthcare organization was able to successfully share data easily with partners during a research project without compromising on data privacy and in line with data protection regulations. The company, which was building research around drug design and solution, needed to share a large data set between hospitals and research partners to make this project successful.

We used Fides to synthesize millions of DNA samples and their respective genomes to a 99.14% fidelity, which in turn allowed for data to be easily and legally shared between partner organizations for drug discovery. This enabled data to be shared among multiple partners (research institutions, computational advertisers, other hospitals, etc.) for comprehensive research while preserving privacy and compliance with stipulated regulations.