Fully Homomorphic Encryption

How to achieve data privacy by design

Research objective

For decades, we have benefitted from modern cryptography to protect our sensitive data during transmission and in storage. However, we have never been able to keep the data protected while it is being processed.

In the past, cryptographic schemes that allowed processing on encrypted data were limited to partial homomorphic schemes that could support only one fundamental operation, namely either addition or multiplication but not both. Then in 2009 IBM pioneered Fully Homomorphic Encryption, which supports both fundamental operations, thus enabling the processing of data without giving access to it, however at this time it was too slow for practical use.

In recent years, thanks to algorithmic advancements, Fully Homomorphic Encryption has reached an inflection point where its performance is becoming practical. This has revolutionized security and data privacy and how we outsource computation to untrusted clouds.

Fully Homomorphic Encryption promises to disrupt major industries such as finance, healthcare, infrastructure and government by unlocking the value of data previously unreachable due to the paradox of need-to-know versus need-to-share between data custodians and data users/exploiters. For example, Fully Homomorphic Encryption makes it possible to share financial data or patient healthcare records for analytics or cross-industry collaboration without giving access to the private data.

IBM Research is closing this gap with the release of HElayers Community Edition, a Software Development Kit (SDK) for the practical and efficient execution of secure AI workloads using Fully Homomorphic Encrypted (FHE) data. To download the HELayers Community Edition Docker Container, which includes sample applications and tutorials in Jupyter Notebooks, for Windows, Linux, macOS and Linux on IBM Z mainframe please use the following link.

HElayers – Community Edition via Docker Hub

To download the HELayers Community Edition Docker Container, including sample applications, tutorials in Jupyter Notebooks and documentation for Windows, Linux, macOS and Linux on IBM Z mainframe please use the following links:

Detailed documentation of the HELayers APIs is available inside the image.

We are interested in your potential use cases and the broader factors driving exploration of FHE. This survey is made available for describing these interests.

If you are interested in learning more about HElayers, or have more questions about FHE, you can reach us at: fhestart@us.ibm.com.