🔒HEIR Compiles Models for Encrypted Data, 2s Latency
Your models now run on encrypted data, but with a catch
TL;DR
HEIR, a new compiler, converts models to run on encrypted data without revealing any information. For a simple fraud detector, it takes 2 seconds on a CPU, compared to 0.5233 ms for cleartext. Debugging is possible with HEIR-specific annotations.
HEIR is a compiler that converts models to operate directly on encrypted data, ensuring no information leaks. For a basic fraud detector, evaluation on encrypted data takes 2 seconds on a single-threaded CPU, while cleartext inference is 0.5233 ms. This matters for developers concerned with data privacy and security. The model uses a three-layer feed-forward network with dimensions 128, 64, and 2, trained on a Kaggle dataset. HEIR can also insert debugging callbacks to inspect ciphertexts for correctness and precision loss. However, manual conversion to MLIR and annotations are required for HEIR to process pre-compiled models.
Key Points
HEIR converts models to run on encrypted data, ensuring no information leaks.
A basic fraud detector takes 2 seconds on a CPU, compared to 0.5233 ms for cleartext.
Model uses a three-layer feed-forward network with dimensions 128, 64, and 2.
Debugging is possible with HEIR-specific annotations for correctness and precision loss.
Manual conversion to MLIR and annotations are required for HEIR to process pre-compiled models.
Why It Matters
If you're developing models for sensitive data, HEIR offers a way to run them on encrypted data without revealing information. For a fraud detector, the 2-second latency on a CPU is a trade-off for privacy. However, the process requires manual conversion to MLIR and annotations, adding complexity. Smaller models benefit more from this approach, while larger models may face longer latencies.
Frequently Asked Questions
Why does this matter?
If you're developing models for sensitive data, HEIR offers a way to run them on encrypted data without revealing information. For a fraud detector, the 2-second latency on a CPU is a trade-off for privacy. However, the process requires manual conversion to MLIR and annotations, adding complexity. Smaller models benefit more from this approach, while larger models may face longer latencies.
What happened?
HEIR, a new compiler, converts models to run on encrypted data without revealing any information. For a simple fraud detector, it takes 2 seconds on a CPU, compared to 0.5233 ms for cleartext. Debugging is possible with HEIR-specific annotations.
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