CipherExplain: Encrypted Explainable AI for Privacy-Preserving ML Interpretability
CipherExplain computes SHAP feature attributions entirely under Fully Homomorphic Encryption (FHE), allowing AI model explanations to stay private while remaining fully interpretable.
Why it matters
CipherExplain solves the critical challenge of providing explainable AI on encrypted data, enabling privacy-preserving interpretability for regulated industries.
Key Points
- 1GDPR and HIPAA require data to stay encrypted, while the EU AI Act requires AI systems to explain their decisions
- 2CipherExplain provides encrypted SHAP feature attributions, ensuring model explanations remain private
- 3Enables interpretable AI in sensitive domains like healthcare, finance, and security
Details
CipherExplain addresses the challenge of providing explainable AI (XAI) on encrypted data, which is required by regulations like GDPR and HIPAA. It computes SHAP feature attributions, a popular XAI technique, entirely under Fully Homomorphic Encryption (FHE). This means the server never sees the plaintext data, but the client still receives a complete explanation of the model's predictions. CipherExplain has been shown to achieve high accuracy compared to standard SHAP, with efficient computation times of around 10 seconds end-to-end on a 50-feature dataset. This technology enables interpretable AI in sensitive domains like healthcare, finance, and security, where data privacy is paramount.
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