Hybrid Privacy Models
Combine masking with differential privacy to add noise to outputs, ensuring individual data points remain untraceable even if masked data is compromised.
Differential Privacy Integration
This layered approach strengthens privacy against inference attacks while maintaining compatibility with Substrate's privacy-preserving mechanisms.
Practical Applications of Differential Privacy
By adding noise to the output of masked computations, we ensure that even if an attacker gains access to the masked data, they cannot reconstruct individual records.
This provides a dual layer of protection that is particularly valuable in scenarios where data sensitivity is high — such as in medical research involving patient records processed through Substrate's secure runtime.
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