On 2026-08-14, Google published an update on HEIR, its open-source compiler toolchain for homomorphic encryption, and showcased four private-inference applications. Homomorphic encryption allows supported computation over encrypted values so that an evaluator need not receive the underlying plaintext. The practical question is not whether the mathematics exists, but whether a complete workload can meet accuracy, latency, cost, and operational requirements.
What HEIR provides
The repository describes an MLIR-based toolchain with Python entry points and multiple schemes and backends, including BGV, BFV, CKKS, CGGI, OpenFHE, Lattigo, tfhe-rs, and Jaxite. Support is not uniform across every scheme/backend pair. The repository also states that HEIR is not an officially supported Google product.
The associated paper, first posted in 2025, presents HEIR as a common compiler structure for combining and comparing homomorphic-encryption techniques. The 2026-08-14 post is therefore an implementation and application update, not the invention date of the project.
Evidence boundary
Google says four peer-reviewed publications have been built on HEIR and describes accelerator partnerships. Those are statements from the project sponsor. They do not establish that every neural network, data type, or production latency target is practical under encryption.
Enterprise evaluation
Start with one narrow computation whose input confidentiality justifies the added complexity. Record supported operations, approximation effects, key management, ciphertext growth, end-to-end latency, failure behaviour, and the trust placed in client and server components. Compare HEIR with alternatives such as local processing, trusted execution environments, secure multiparty computation, or minimising the data collected.
HEIR lowers the engineering barrier to experimentation. Production suitability still requires workload-specific measurement and a cryptographic review of the chosen scheme, parameters, and implementation.