Finance, healthcare, biotech, and regulated AI.
- Protect proprietary signals and sensitive records while enabling AI development.
- Support reviewable release workflows for legal, compliance, security, and data-owner stakeholders.
Privacy-preserving synthetic data
The safest useful version of your data.
Morph Research builds privacy-preserving synthetic data, differential privacy, privacy evaluation, and release-readiness workflows for finance, healthcare, biotech, and other teams working with sensitive and regulated AI data.
Finance · Healthcare · Biotech · Regulated AI
§ 01 — The release pipeline
From sensitive source to defensible release.
Morph Research helps teams figure out the safest useful version of their data and what evidence is needed before release, sharing, or downstream AI use.
01
Synthesize
Build synthetic data workflows for regulated, temporal, transaction, clinical, biomedical, and event-log settings where direct data use is hard, risky, or operationally constrained.
OutputSynthetic dataset
02
Guarantee
Differential privacy is part of how Morph approaches safe release, including practical deployment experience in time-series and other high-sensitivity settings.
Output(ε, δ) proof
03
Attack
Attack the privacy boundary before data leaves the secure environment: leakage checks, membership and attribute attacks, similarity review, and release-readiness evaluation.
OutputEvaluation report
04
Release
Ship the dataset with its evidence — what was protected, how it was tested, and why legal, compliance, security, and data-owner reviewers can sign off.
OutputSign-off packet
§ 02 — Solutions
Built for high-value, high-sensitivity data.
Whether the challenge is safe sharing, model development, forecasting, or internal evaluation, Morph Research focuses on data regimes where privacy decisions materially change what is possible.
Time-series, transaction, and event-log workflows.
- Handle forecasting, sequence modeling, rare-regime analysis, and evaluation for temporal data.
- Bring privacy-preserving methods to workflows where row order, events, and trajectories matter.
§ 03 — Research
Research translated into deployable systems.
The mission is not just to prove that privacy-preserving methods work. It is to make them practical enough that teams can actually choose them.
ICML 2026 · Poster
View resources →
Geometry-Aware Tabular Diffusion
The paper, code, datasets, and reproducibility artifacts for GATD — a stable home for the ICML 2026 poster resources.
“If we can make privacy-enhancing technologies faster and less expensive to deploy than non-private systems, it becomes much easier for practitioners to choose the privacy-preserving path.”
David Turtora Zagardo · Founder
Research background
Morph Research is informed by work in privacy-preserving machine learning, synthetic data, differential privacy, privacy evaluation, and release readiness for sensitive AI systems.
That includes geometry-aware synthetic data research, time-series privacy work, and a practical emphasis on turning formal privacy ideas into systems teams can understand, trust, and deploy.
Design-partner access
Bring a hard, sensitive-data workflow.
Morph Research is preparing limited design-partner conversations for teams working on sensitive time-series, transaction, clinical, biomedical, or event-log data.
If you are exploring synthetic data, differential privacy deployments, privacy evaluation, or release-readiness review, reach out and describe the workflow.