The toolkit is currently available for iOS and macOS users but IBM noted that it will be coming to Android and Linux users in the future. The kit is available on GitHub (iOS, macOS) but IBM has cautioned that it is not final yet and may contain bugs or glitches that might impact the overall performance.
FHE holds significant promise for a number of use cases such as extracting value from private data; data set intersection ; genomics analytics; oblivious queries (i.e. querying without revealing intent) and secure outsourcing.
FHE is particularly suited to industries which are regulated and make use of private, confidential and “crown jewel” data, such as finance and healthcare, since the technology can make it possible to share financial information or patient health records broadly while restricting access to all but the necessary data.
For example, we recently published a paper with Brazil’s Banco Bradesco SA, where we homomorphically encrypted the data and the model, and showed that it was possible to run predictions with the same accuracy as without encryption and with adequate performance. The result, banks can safely outsource the task of running predictions to an untrusted environment.