Data Synthesis: Empowering Model Creation Without Original Data Exposure

Create a process of data synthesis by developing a comprehensive service and Python package incorporating various models for generating synthetic data from diverse sources.

Starting Point

Our client faced challenges in accessing or sharing sensitive data for model development, prompting us to develop a solution that could generate synthetic data without compromising privacy or security.

Objective

Our primary objective was to provide a robust framework that allow our client to generate synthetic data from different sources, facilitating the development of new models.

Added Value​

By offering a comprehensive service and Python package equipped with state-of-the-art data synthesis models, we provided our client with the means to advance their research and development efforts while maintaining data privacy and confidentiality.

From challenges to solutions

Model Accuracy

Ensuring that synthetic data accurately represents the underlying patterns and distributions of the original data sources.

Scalability

Adapting the solution to handle large and diverse datasets efficiently while maintaining performance.

Privacy Preservation

Guaranteeing that the generated synthetic data preserves privacy and does not inadvertently reveal sensitive information.

Continuous Model Improvement

Implementing iterative refinement techniques and rigorous validation processes to enhance the accuracy and fidelity of synthetic data generated by our models.

Optimized Processing

Employing parallel processing and optimization strategies to enhance the scalability of our solution, allowing it to handle increasingly large and complex datasets.

Privacy-Preserving Mechanisms

Implementing differential privacy techniques and data masking strategies to ensure that the synthetic data produced by our models does not compromise the privacy or confidentiality of the original information

Technical deep dive​

Dive deep into our work on Data Synthesis

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