Securing Health Data on the Blockchain with DP and Federated Learning
The 20-Second Summary
Healthcare IoT data is valuable for analytics, but centralizing patient data creates major privacy and compliance risk. This work combines federated learning (to keep data local) with differential privacy (to reduce leakage from updates) and a blockchain audit trail (to record aggregation events). In the reported experiments, the system reaches 64.50% accuracy on SVHN at $\varepsilon = 8.0$ after 15 FL rounds, with ~6 seconds average blockchain transaction latency (arXiv 2024).
The Problem
Healthcare systems generate large volumes of data from wearable monitors, smart infusion pumps, connected imaging devices, and remote patient sensors. Training machine learning models on this data could improve diagnostics, personalize treatment, and detect deterioration early.
But there’s a catch: healthcare data is among the most sensitive data that exists. A patient’s vital signs, medication records, and diagnostic images are protected by HIPAA (US), GDPR (EU), and similar regulations globally. Centralizing this data for model training creates:
privacy risk (a single breach can expose thousands of records), compliance liability (moving data across institutions triggers regulatory obligations), trust erosion (patients may lose confidence when data is pooled for ML), and legal exposure (penalties for mishandling protected health information).
Federated learning addresses the data movement problem - models train locally and only share updates. But FL alone is not enough. Model updates can leak information about training data through gradient inversion, membership inference, and attribute inference attacks. And without an auditable record of which updates were aggregated, there’s no way to verify the training process after the fact.
This work proposes a system that combines privacy protection at the data level, verifiability at the aggregation level, and utility at the model level.
Why Existing Approaches Fall Short
| Approach | Limitation |
|---|---|
| Centralized ML | Requires data pooling - privacy and regulatory non-starter for healthcare |
| Federated learning alone | Updates can leak patient information through inference attacks |
| FL + secure aggregation | Protects updates in transit but leaves no audit trail |
| FL + differential privacy | Protects privacy but has no verifiable aggregation record |
| Blockchain for health records | Immutable storage, but no ML capability |
The core issue: no single technique provides privacy, utility, and auditability simultaneously. FL handles data locality. DP handles update privacy. Blockchain handles aggregation integrity. We need all three working together.
Our Approach: FL + Differential Privacy + Blockchain
The framework integrates three layers, each addressing a different aspect of the privacy-utility-trust triangle.
Layer 1: Federated Learning for Data Locality
Each healthcare institution (hospital, clinic, wearable device hub) trains a model on its local patient data. Only model updates - gradient vectors or parameter deltas - are communicated to the aggregation server. Raw patient data never leaves the institution.
We use a standard federated averaging (FedAvg) protocol:
1. Server distributes global model to all participating institutions
2. Each institution trains on local data for E local epochs
3. Institutions send model updates (not data) to the server
4. Server aggregates updates into a new global model
5. Repeat for R rounds
Layer 2: Differential Privacy for Update Protection
Before transmitting updates, each institution applies local differential privacy (LDP). This involves two operations:
gradient clipping (bounding the $\ell_2$ norm of the update to limit the influence of any single patient) followed by calibrated Gaussian noise addition.
The privacy budget ε controls the privacy-utility tradeoff. Smaller ε means more noise and stronger privacy but lower model accuracy. Larger ε means less noise and better accuracy but weaker privacy guarantees.
We report results at ε = 8.0, which provides a meaningful privacy guarantee while maintaining useful model performance. This is a practical operating point - strong enough to resist common inference attacks, but not so aggressive that the model becomes useless.
Layer 3: Blockchain for Auditable Aggregation
The aggregated model updates are recorded on a blockchain, creating an immutable audit trail. This serves multiple purposes:
it makes inclusion tamper-evident (integrity), supports post-hoc verification (transparency), helps institutions prove contribution/non-contribution (non-repudiation), and makes manipulation detectable (tamper evidence).
The blockchain stores hashes of aggregated updates and metadata (round number, participating institutions, privacy parameters), not the full model weights. This keeps on-chain storage manageable.
How It All Fits Together
IoT Device → Local Training (FedAvg) → DP Noise Addition (ε = 8.0)
→ Update Submission → Server Aggregation
→ Blockchain Recording (hash + metadata)
→ New Global Model Distribution
How We Evaluated
Dataset: SVHN (Street View House Numbers) - a challenging real-world image recognition task with 10 classes. While not a clinical dataset, SVHN provides a rigorous benchmark for evaluating the privacy-utility tradeoff because its complexity stresses the model under noisy conditions.
FL Configuration:
We run 15 federated learning rounds with multiple participating clients simulating healthcare institutions, using FedAvg aggregation and local differential privacy.
Privacy Settings: ε = 8.0 with gradient clipping. We chose this operating point to demonstrate a realistic privacy-utility balance.
Blockchain Environment: A test blockchain network measuring transaction latency and gas consumption for update recording.
Metrics:
We report SVHN test accuracy and end-to-end round time, the formal DP privacy budget $\varepsilon$, plus blockchain-side latency and gas usage for recording aggregation events.
Key Results
The reported results show the expected privacy–utility trade-off while keeping the audit trail practical. Accuracy reaches 64.50% on SVHN after 15 rounds at $\varepsilon = 8.0$: DP noise reduces accuracy relative to non-private training, but the model still learns useful signal. On the audit side, average blockchain transaction latency is about 6 seconds with stable gas usage across rounds, suggesting the blockchain layer scales roughly linearly with the number of aggregation events.
Why This Matters
Healthcare AI is moving from research to deployment, and the privacy infrastructure must keep pace. This work demonstrates that it’s possible to build a system where:
Patient data stays local (no central repository to breach), updates get formal privacy protection (DP rather than informal promises), and the training process becomes auditable (a tamper-evident record regulators can inspect). At the same time, model utility remains measurable (64.50% on SVHN under the reported settings). Conceptually, the layers address different risks: locality reduces exposure, DP limits leakage, and blockchain provides tamper evidence.
Limitations and Next Steps
Current Limitations:
SVHN is not clinical data, so validation on real healthcare modalities (X-rays, ECGs, EHR tables) is still needed. The chosen $\varepsilon = 8.0$ is a moderate privacy setting; more sensitive contexts may require lower $\varepsilon$ with corresponding accuracy impacts. The blockchain measurements come from a test environment (production networks/permissioned deployments may differ), and 15 rounds is modest—longer runs are needed to assess convergence under DP noise.
Future Work:
Next steps are straightforward: evaluate on real clinical datasets with domain-appropriate models, explore tighter privacy–utility trade-offs (e.g., Rényi DP and privacy amplification), test on permissioned enterprise chains, and layer secure aggregation for defense in depth. A particularly important systems question is how DP noise interacts with fairness across patient demographics.