Google releases HEIR, an open-source compiler for private AI inference using homomorphic encryption

Introducing HEIR for Private AI

Google has announced the release of HEIR, an open-source compiler toolchain. HEIR, which stands for Homomorphic Encryption Intermediate Representation, is designed to facilitate cryptographically-secure private AI inference. This new tool is part of Google's broader Private Computing Toolkit.

Addressing Privacy in AI

The growth of AI has highlighted the need to balance privacy and security, especially with user data. Traditional encryption methods protect data but prevent service providers from performing data-dependent functions like spam detection. Homomorphic encryption offers a solution by allowing computations directly on encrypted data, meaning servers can process information and return encrypted results without ever seeing the raw data. This capability is particularly relevant for sensitive sectors such as healthcare and finance, which face strict data sharing regulations.

The Role of Homomorphic Encryption

Homomorphic encryption fundamentally changes the privacy-capability trade-off by enabling operations on encrypted data. While it incurs a cost overhead, this cost is decreasing. Google has a history of innovations in privacy technology, including differential privacy and private information retrieval, and views homomorphic encryption as another cryptographic tool to secure user data. Unlike hardware-based solutions, homomorphic encryption provides strong security and privacy guarantees purely through cryptography.

Simplifying Homomorphic Encryption Use

A significant challenge with homomorphic encryption has been its complexity, often requiring specialized cryptographic expertise to implement efficiently. The HEIR compiler project was developed by Google researchers and engineers to overcome these usability barriers. By providing an open-source compiler, HEIR aims to make homomorphic encryption more accessible and practical for a wider range of developers and applications, thereby advancing its adoption in private AI inference.