Google open sources HEIR to make homomorphic encryption usable for AI

[Pale Connor]

Google published a rundown of HEIR, an open source compiler they built to make homomorphic encryption actually usable for AI applications instead of something only a handful of specialist cryptographers can implement. The basic idea behind homomorphic encryption is that a server can run computations directly on encrypted data and hand back an encrypted result without ever seeing the underlying information, which sounds almost like a contradiction but is apparently a maturing area of cryptography

Google's pitch is that HEIR can take a pre trained AI model built to run on regular unencrypted data and convert it to operate on encrypted inputs instead, with the long term goal of making that conversion close to a one click process for non experts. That matters because right now, manually converting a program to use homomorphic encryption efficiently basically requires its own dedicated team of cryptographers, which puts it out of reach for most companies

They shared four demo applications built with HEIR to show how far this has come, including a content recommendation model that can serve suggestions without seeing a user's actual data, a credit card fraud detector, a network intrusion detection system that can flag anomalies without exposing the contents of the traffic it is analyzing, and a hotword detector for audio triggered AI agents

HEIR has apparently been picked up as a research platform too, with collaborations listed from Georgia Tech, Carnegie Mellon, UC Santa Barbara, Purdue, the University of Edinburgh and Tsinghua University among others, plus partnerships with hardware accelerator companies aiming to bring down the latency cost that homomorphic encryption is known for

The framing throughout is that this shifts the old privacy versus capability tradeoff into more of a cost question, and that cost is apparently dropping fast enough that this could become a genuinely practical option rather than a theoretical one

How Google is Making Private AI Practical with Homomorphic Encryptionblog.google

[Quarry16]

The fraud detection and network intrusion demos are the ones that actually sell me on this, being able to flag anomalies without ever exposing the raw traffic contents is an useful privacy guarantee for security teams

[Saka31]

Curious how much latency overhead is still baked in even with the hardware accelerator partnerships, that has always been the real bottleneck holding this technology back from production use

[RightNutter82]

Would love to see real world latency benchmarks comparing this against a normal unencrypted pipeline for something like the hotword detector, the numbers in the announcement itself feel a bit vague on that front

[Merchant27]

Open sourcing the compiler is a smart move if Google actually wants broad adoption instead of just showing off their own research, a closed tool would never get this kind of academic and industry pickup

[Ridge]

The recommendation engine demo is a good example of the tradeoff, a cloud service that can suggest content without ever seeing your data sounds almost too good to be true given how ad targeting usually works

[Niamh88]

Feels like this could eventually change how AI companies think about handling sensitive data entirely, though four peer reviewed publications is still a pretty early stage for something being pitched this confidently

[Inlet]

The framing that this turns a privacy versus capability tradeoff into a cost question is the key insight here, cost problems tend to get solved over time in a way that fundamental tradeoffs do not