In an increasingly data-driven world, the tension between leveraging powerful AI models and protecting sensitive user information is growing. How can we perform complex computations on data without ever exposing the underlying sensitive details? This chapter introduces you to a groundbreaking solution: Homomorphic Encryption (HE) and HEIR, an open-source compiler designed to make privacy-preserving AI a practical reality.

We’ll dive into the core concepts of Homomorphic Encryption, understand why Fully Homomorphic Encryption (FHE) is particularly challenging, and then explore HEIR’s role as an “end-to-end FHE compiler.” You’ll learn about its architecture, its current development status (as of 2026-08-18), and how it aims to bridge the gap between AI models and secure, encrypted computation. This knowledge is crucial for any developer looking to build the next generation of privacy-first AI applications.

The Privacy Conundrum in AI

Imagine you have a powerful AI model that can detect diseases from medical images, but the images contain highly sensitive patient data. Or perhaps you want to run a fraud detection model on financial transactions, but privacy regulations prevent you from directly accessing raw customer data. The traditional approach requires decrypting data, processing it, and then re-encrypting it, creating a vulnerable window where data is exposed.

This exposure is a significant hurdle for deploying AI in sensitive domains like healthcare, finance, or government. It’s a problem that Homomorphic Encryption seeks to solve by allowing computations directly on encrypted data.

Homomorphic Encryption (HE) vs. Fully Homomorphic Encryption (FHE)

Before we meet HEIR, let’s clarify the fundamental cryptographic concepts it builds upon. Understanding these distinctions is key to appreciating HEIR’s purpose.

Homomorphic Encryption (HE)

📌 Key Idea: Homomorphic Encryption allows computations on encrypted data without decrypting it first.

Think of it like a secure processing facility. You send encrypted data to this facility. Inside, operations are performed on the encrypted data. The facility never sees the original sensitive information, only its scrambled form. When the encrypted result is returned to you, only you can decrypt it to reveal the plaintext outcome.

However, traditional HE schemes often have limitations:

  • Limited Operations: Some schemes might only support addition, or only multiplication, but not both. This restricts the types of programs you can run.
  • Finite Operations: They might only allow a fixed number of operations before the “noise” in the ciphertext grows too large. This “noise” is an inherent part of HE and, if unchecked, can make decryption impossible.

Fully Homomorphic Encryption (FHE)

This is where the “Fully” comes in, and it’s a game-changer for AI.

🧠 Important: FHE allows any arbitrary computation to be performed on encrypted data, an unlimited number of times, without ever decrypting it. It achieves this through a process called “bootstrapping,” which essentially “refreshes” the ciphertext to reduce noise.

This means you can run an entire AI inference model – which involves many additions, multiplications, and other complex operations – entirely on encrypted inputs. The model’s weights themselves can also be encrypted, adding another layer of privacy. The user provides encrypted data, the cloud server processes it while it remains encrypted, and returns an encrypted result. Only the user can decrypt the final output.

The challenge? FHE is computationally intensive. Operations on encrypted data are significantly slower and require more memory than operations on plaintext data. This is why “compilers” like HEIR are so crucial: they aim to optimize FHE computations to make them practical.

Introducing HEIR: The FHE Compiler

HEIR (Homomorphic Encryption Intermediate Representation) is an open-source project that aims to be an end-to-end compiler for Fully Homomorphic Encryption programs. It’s designed to take a program written for plaintext execution (like an AI model) and transform it into an optimized FHE program that can run securely on encrypted data.

What Problem Does HEIR Solve?

HEIR tackles the complexity and inefficiency of FHE directly. Manually translating an AI model into an FHE-compatible circuit and then optimizing it for performance is incredibly difficult and error-prone. HEIR aims to automate this process, allowing developers to focus on their AI logic rather than the intricate details of FHE cryptography.

⚡ Real-world insight: Google is involved in the development of HEIR. While the project is open-source and community-driven, Google’s contributions signify a commitment to advancing the practical application of FHE for private AI, particularly in cloud environments where data privacy is paramount.

How HEIR Works (Conceptual Flow)

At its core, HEIR functions much like a traditional compiler, but with a specific focus on FHE. It translates high-level code into an optimized form suitable for FHE execution.

Here’s a simplified view of its conceptual stages:

flowchart LR A[Source Code ML Model] -->|Front-End passes| B(High-Level IR) B -->|Middle-End passes| C(Optimized FHE IR) C -->|Back-End passes| D[Target FHE Library Code]
  1. Front-End: This stage takes your original program (e.g., an ML model defined in a common format or a high-level language) and converts it into an initial, high-level Intermediate Representation (IR). Think of IR as a standardized, abstract way to represent your program’s operations.
  2. Middle-End: Here, the real FHE magic begins. This stage performs FHE-specific optimizations on the IR. This might involve reordering operations, batching data to utilize FHE’s parallel computation capabilities, or selecting specific FHE schemes and parameters to improve efficiency and manage noise.
  3. Back-End: Finally, the optimized FHE IR is translated into concrete code that can be executed by an existing FHE library (e.g., SEAL, HElib, Concrete). These libraries handle the actual cryptographic operations.

This layered approach allows HEIR to integrate with various FHE libraries and target different hardware, providing flexibility and future-proofing as FHE technology evolves.

HEIR’s Current Status (as of 2026-08-18)

It’s important to understand that HEIR is an actively developed, cutting-edge project. As such, its capabilities and documentation are continually evolving. This means that while it’s a powerful research and development tool, its full end-to-end capabilities are still maturing.

The official HEIR GitHub repository provides the most up-to-date information. As of our check on 2026-08-18, the repository notes a critical limitation for developers:

⚠️ What can go wrong: The integration between HEIR’s Middle-End and Back-End is currently not yet well-implemented. This means that while HEIR can process and optimize the Intermediate Representation, generating a fully executable FHE program that links directly to an FHE library might require manual intervention or specific tools. The documentation specifically mentions that “If you require an executable, please use format_assistant/h.” This indicates that direct, seamless compilation to a runnable FHE program is still under active development.

This limitation means that while HEIR is a powerful tool for understanding and optimizing FHE computation graphs, it’s not yet a “black box” compiler where you feed in an AI model and get a runnable encrypted binary without further steps. Developers should expect to engage with the generated IR and potentially use helper tools or manual integration with FHE libraries for full end-to-end execution.

Step-by-Step Implementation: Setting Up HEIR (Conceptual Build)

While generating a fully executable FHE program is still evolving, you can still clone and build the HEIR compiler itself to explore its components and contribute to its development. This will give you the tools to work with HEIR’s Intermediate Representation.

Prerequisites

To build HEIR, you’ll need CMake, a cross-platform build system. CMake helps manage the compilation process for complex projects.

  • CMake: Ensure you have CMake version 3.20 or newer installed.
    • On Ubuntu/Debian:
      sudo apt update && sudo apt install cmake
    • On macOS (with Homebrew):
      brew install cmake
    • For other systems, refer to the official CMake documentation for installation instructions.

Cloning the HEIR Repository

First, let’s get the source code from GitHub. This is where the entire HEIR project resides.

# Clone the HEIR repository from GitHub
git clone https://github.com/heir-compiler/HEIR.git

# Navigate into the newly cloned directory
cd HEIR

This command downloads the entire project to your local machine, creating a directory named HEIR.

Building HEIR

Now, let’s configure and build the project using CMake. This process will compile the HEIR compiler itself, along with its various passes and tools that operate on FHE IR.

  1. Create a build directory: It’s good practice to build outside the source directory to keep things clean.

    mkdir build
cd build

  2. Configure the project with CMake: This command tells CMake to look for the CMakeLists.txt file in the parent directory (..) and generate platform-specific build files (e.g., Makefiles on Linux/macOS, Visual Studio solutions on Windows).

    cmake ..

    You might see output indicating that CMake is finding dependencies and configuring the build environment. If there are missing dependencies (like LLVM/MLIR components), CMake will usually report them here.

  3. Build the project: This command starts the actual compilation process using the build files generated by CMake.

    cmake --build .

    This might take a few minutes, depending on your system’s specifications and the number of available processor cores. You’ll see compiler output as it builds the various components of HEIR.

    Upon successful completion, you will find HEIR executables and libraries within your build directory. For example, build/bin/heir-opt might be one of the key tools, allowing you to run various optimization passes on FHE IR.

Mini-Challenge: Exploring the IR

Now that you’ve successfully built HEIR, let’s take a closer look at its internal structure. Understanding where different components live can help demystify how compilers work.

Challenge: Navigate into the build/lib and build/bin directories. Can you identify where the Intermediate Representation (IR) definitions might be located, or where the compiler’s optimization passes are implemented?

Hint:

  1. Start by looking at the original source directories: HEIR/lib/Dialect and HEIR/lib/Transforms.
  2. Then, observe how these source components are organized and compiled into the build/lib directory (e.g., .so or .dylib files).
  3. In build/bin, look for executables that suggest they can process or optimize IR, like heir-opt.

What to Observe/Learn: This exercise helps you understand the modular nature of a compiler. You’ll see how different components are compiled into libraries and executables, giving you a glimpse into where the “brains” of the FHE optimization live. This hands-on exploration reinforces the conceptual flow we discussed earlier.

Common Pitfalls & Troubleshooting

Working with an experimental compiler like HEIR can present unique challenges. Here are some common issues and how to approach them.

  1. Dependency Hell:

    • Problem: CMake errors indicating missing required libraries (e.g., specific versions of LLVM, MLIR, or other development tools).
    • Solution: Carefully read the error messages. HEIR often relies on specific versions of MLIR/LLVM, which can be tricky to manage. Always check the HEIR/README.md and HEIR/docs within the cloned repository for the exact dependency requirements and recommended installation methods. You might need to install these manually or via your system’s package manager, sometimes even building them from source if specific versions are required.

  2. “Not Yet Well-Implemented” Backend:

    • Problem: You’ve compiled HEIR, but you’re struggling to generate a fully executable FHE program from your AI model that runs end-to-end.
    • Solution: Remember HEIR’s current development status (as of 2026-08-18). The project is a powerful tool for generating and optimizing FHE IR, but its direct, seamless compilation to runnable FHE programs is still an area of active research. Focus on using HEIR to understand and manipulate the IR, and then manually integrate that IR with a specific FHE library (like SEAL or HElib) using helper tools such as format_assistant/h as mentioned in the HEIR documentation. This approach requires a deeper understanding of FHE libraries.

  3. Evolving API and Documentation:

    • Problem: Code examples or documentation you find online from blogs or older tutorials are outdated and no longer work with the latest HEIR version.
    • Solution: Always refer to the official HEIR GitHub repository as the primary source of truth. The main branch is the most current, and the docs directory within the repository will have the latest guides and examples. Given the rapid pace of development in this field, frequent checks against the official source are essential.

Summary

In this chapter, we’ve taken a significant step into the world of privacy-preserving AI by introducing HEIR.

Here are the key takeaways:

  • We distinguished between Homomorphic Encryption (HE), which allows limited operations on encrypted data, and Fully Homomorphic Encryption (FHE), which enables arbitrary, unlimited computations on encrypted data.
  • We learned that HEIR is an open-source compiler designed to bridge the gap between AI models and FHE, aiming to make secure, encrypted computation practical by optimizing the complex FHE operations.
  • We discussed HEIR’s conceptual architecture, involving Front-End, Middle-End, and Back-End stages that transform high-level programs into FHE-compatible code.
  • Crucially, we acknowledged HEIR’s current development status (as of 2026-08-18), noting that while powerful, its end-to-end compilation to executable FHE programs is still evolving and may require manual integration with FHE libraries.
  • Finally, we walked through the basic steps to build HEIR from its source code and explored its structure through a mini-challenge, preparing you to dive deeper into its capabilities.

The journey into privacy-preserving AI with FHE is just beginning. In the next chapter, we’ll start exploring how to define simple computations and how they might be represented within HEIR’s Intermediate Representation, laying the groundwork for more complex AI applications.

References

  1. HEIR GitHub Repository: https://github.com/heir-compiler/HEIR
  2. A Guide For HEIR Experiment Evaluation (Original Version): https://github.com/heir-compiler/HEIR/raw/refs/heads/main/README.md
  3. CMake Official Documentation: https://cmake.org/documentation/

This page is AI-assisted and reviewed. It references official documentation and recognized resources where relevant.