Privacy-hardened and hallucination-resistant synthetic data generation with logic-solvers.

Area of Science:

• Bioinformatics
• Computational Biology
• Data Science

Background:

• Synthetic data generation is crucial for AI training and data sharing, but current methods face challenges with large datasets, realism, and privacy quantification.
• Existing techniques struggle to handle complex data like genomics, often producing unrealistic outputs or failing to guarantee privacy.
• There is a need for scalable and privacy-preserving methods for generating high-fidelity synthetic data, especially for sensitive information.

Purpose of the Study:

• To introduce Genomator, a novel logic-solving approach for generating private and realistic synthetic genomic data.
• To demonstrate Genomator's effectiveness in benchmarking against state-of-the-art synthetic data generation methods.
• To highlight Genomator's scalability and tuneability for various applications in genomics and beyond.

Main Methods:

• Genomator employs a logic-solving (SAT solving) approach to generate synthetic data.
• The method was applied to complex genomic data, a challenging and privacy-sensitive information type.
• Performance was benchmarked against Markov generation, Wasserstein Generative Adversarial Networks, and Conditional Restricted Boltzmann Machines.

Main Results:

• Genomator achieved 40%-530% higher accuracy and 57%-172% greater privacy compared to existing methods.
• The approach is 3-100 times more efficient, enabling scalability to whole genomes.
• Genomator demonstrated a tunable trade-off between privacy and accuracy, catering to diverse application needs.

Conclusions:

• Genomator offers an efficient, scalable, and privacy-preserving solution for synthetic data generation, particularly for complex genomic data.
• The method's ability to balance privacy and accuracy makes it suitable for a wide range of applications, from sensitive cohort analysis to pharmacogenomic profiling.
• The production-scale generation of tunable synthetic genomes has significant implications for medical research, data exchange, and addressing population underrepresentation.