🔒Google Unveils HEIR: Open-Source Compiler for Homomorphic Encryption
End-to-end encryption just got a major boost from Google
TL;DR
Google introduces HEIR, an open-source toolchain enabling AI models to operate on encrypted data. It supports private content recommendations and credit card fraud detection without exposing user info.
Google just launched HEIR (Homomorphic Encryption Intermediate Representation), an open-source compiler that lets pre-trained AI models work with encrypted data directly. This is a big deal for developers working in privacy-sensitive applications, as it allows services like spam detection to operate on encrypted user data without compromising security. With HEIR, application developers can write Python code and annotate which data types should be encrypted, then compile the program for homomorphic execution. A 64-bit equality operation takes approximately 80ms, while addition and subtraction operations take around 100ms.
Key Points
HEIR uses torch_mlir to export PyTorch models to MLIR format for FHE compilation.
A 64-bit equality operation takes approximately 80ms in HEIR's implementation.
Addition and subtraction operations take around 100ms, while division can take up to 8 seconds.
LLMs may see better performance due to reliance on addition and multiplication over branching.
Google has used HEIR for private content recommendations, fraud detection, intrusion identification.
Why It Matters
If you're working with sensitive data in AI applications, HEIR can enable end-to-end encryption without sacrificing functionality. For instance, a spam detection service could operate on encrypted user data using HEIR's compiler toolchain. However, the performance overhead means that some tasks might see significant delays — for example, image classification tasks could go from 1ms to 1 second with a 1000x overhead.
Frequently Asked Questions
Why does this matter?
If you're working with sensitive data in AI applications, HEIR can enable end-to-end encryption without sacrificing functionality. For instance, a spam detection service could operate on encrypted user data using HEIR's compiler toolchain. However, the performance overhead means that some tasks might see significant delays — for example, image classification tasks could go from 1ms to 1 second with a 1000x overhead.
What happened?
Google introduces HEIR, an open-source toolchain enabling AI models to operate on encrypted data. It supports private content recommendations and credit card fraud detection without exposing user info.
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